Springer, 2022. — 528 p. — ISBN: 978-3-030-92525-3. This book focuses on neuromorphic computing principles and organization and how to build fault-tolerant scalable hardware for large and medium scale spiking neural networks with learning capabilities. In addition, the book describes in a comprehensive way the organization and how to design a spike-based neuromorphic system to...
CRC Press, 2023. — 324 p. — ISBN: 978-1-032-35988-5. More frequent and complex cyber threats require robust, automated, and rapid responses from cyber-security specialists. This book offers a complete study in the area of graph learning in cyber, emphasizing graph neural networks (GNNs) and their cyber-security applications. Three parts examine the basics, methods and...
Springer, 1997. — 266 p. Neural Networks and Fuzzy Systems: Theory and Applications discusses theories that have proven useful in applying neural networks and fuzzy systems to real world problems. The book includes performance comparison of neural networks and fuzzy systems using data gathered from real systems. Topics covered include the Hopfield network for combinatorial...
IOS Press, 2003. — 334 p. The aims of this book are to disseminate wider and in-depth theoretical and practical knowledge about neural networks in measurement, instrumentation and related industrial applications, to create a clear consciousness about the effectiveness of these techniques as well as the measurement and instrumentation application problems in industrial...
Amsterdam: IOS Press, 2003. — 256 p. — (NATO Science Series). — ISBN: 1-58603-324-7. This work reports critical analyses on complexity issues in the continuum setting and on generalization to new examples, which are two basic milestones in learning from examples in connectionist models. The problem of loading the weights of neural networks, which is often framed as continuous...
New York: Springer, 2017. — 299 p. This must-read text/reference introduces the fundamental concepts of convolutional neural networks (ConvNets), offering practical guidance on using libraries to implement ConvNets in applications of traffic sign detection and classification. The work presents techniques for optimizing the computational efficiency of ConvNets, as well as...
Springer, 2014. — 176 p. This book addresses the new models in mathematical neuroscience and artificial neural networks, which have many similarities with the structure of the human brain and the functions of cells by electronic circuits. The networks have been investigated due to their extensive applications in classification of patterns, associative memories, image...
World Scientific Publishing, 2018. — 221 p. — ISBN: 978-981-3233-42-3. The Boolean network (BN) is a mathematical model of genetic networks and other biological networks. Although extensive studies have been done on BNs from a viewpoint of complex systems, not so many studies have been undertaken from a computational viewpoint. This book presents rigorous algorithmic results on...
Academic Press, 2019. — 165 p. — ISBN: 978-0-12-818247-5. This book presents current trends for the solution of complex engineering problems that cannot be solved through conventional methods. The proposed methodologies can be applied to modeling, pattern recognition, classification, forecasting, estimation, and more. Readers will find different methodologies to solve various...
Academic Press, 2019. — 165 p. — ISBN: 978-0-12-818247-5. Artificial Neural Networks for Engineering Applications presents current trends for the solution of complex engineering problems that cannot be solved through conventional methods. The proposed methodologies can be applied to modeling, pattern recognition, classification, forecasting, estimation, and more. Readers will...
New Delhi: New Age Publishers, 2008. — 276 p. This book attempts to provide the reader with basic concepts and engineering applications of Fuzzy Logic and Neural Networks. Some of the material in this book contains timely material and thus may heavily change throughout the ages. The choice of describing engineering applications coincides with the Fuzzy Logic and Neural Network...
Springer, 2006. — 258 p. — ISBN: 978-0387-33415-8. Artificial neural networks (ANNs) offer a general framework for representing non-linear mappings from several input variables to several output variables, and they can be considered as an extension of the many conventional mapping techniques. In addition to many considerations on their biological foundations and their really...
Springer, 2006, -265 p. Artificial neural networks (ANNs) offer a general framework for representing non-linear mappings from several input variables to several output variables, and they can be considered as an extension of the many conventional mapping techniques. In addition to many considerations on their biological foundations and their really wide spectrum of...
Elsevier, 2002. — 387 p. This volume is concerned with the analysis and interpretation of multivariate measurements commonly found in the mineral and metallurgical industries, with the emphasis on the use of neural networks.The book is primarily aimed at the practicing metallurgist or process engineer, and a considerable part of it is of necessity devoted to the basic theory...
Columbia University Press, 2001, 205 p. One of the earliest proponents of neural engineering to build artificially intelligent systems, Aleksander (Imperial Coll. of Science, Technology, and Medicine, London) has more than 30 years of artifical intelligence research under his belt. Though he covers a substantial amount of engineering as it applies to building machines with...
Nova Science Pub Inc, 2020. — 234 p. — (COMPUTER SCIENCE, TECHNOLOGY AND APPLICATIONS). — ISBN: 9781536171884. “With respect to the ever-increasing developments in artificial intelligence and artificial neural network applications in different scopes such as medicine, industry, biology, history, military industries, recognition science, space, machine learning and etc., Neural...
Springer, 2014. — 203 p. Ordinary neuro-fuzzy systems (i.e. type:1 neuro-fuzzy systems) have been successfully used in a wide range of applications. To design ordinary neuro-fuzzy systems, knowledge of human experts and experimental data are needed for construction of fuzzy rules and membership functions based on available linguistic or numeric information. However, in many...
Springer, 2022. — 336 p. This book goes into a detailed investigation of adapting artificial neural network (ANN) and structural equation modeling (SEM) techniques in marketing and consumer research. The aim of using a dual-stage SEM and ANN approach is to obtain linear and non-compensated relationships because the ANN method captures non-compensated relationships based on the...
Springer, 2019. — 315 p. — ISBN: 978-3-030-24454-5. This book provides a state-of-the-art overview on the dynamics and coevolution in multi-level strategic interaction games. As such it summarizes the results of the European CONGAS project, which developed new mathematical models and tools for the analysis, prediction and control of dynamical processes in systems possessing a...
Cambridge: Cambridge University Press, 1989. — 263 p. Exploring one of the most exciting and potentially rewarding areas of scientific research, the study of the principles and mechanisms underlying brain function, this book introduces and explains the techniques brought from physics to the study of neural networks and the insights they have stimulated. Substantial progress in...
Springer, 2011, -116 p. This brief monograph is the first one to deal exclusively with the quantitative approximation by artificial neural networks to the identity-unit operator. Here we study with rates the approximation properties of the right sigmoidal and hyperbolic tangent artificial neural network positive linear operators. In particular we study the degree of...
Cambridge: MIT Press, 1997. — 650 p. — ANDJP 0-262-51081-2. An Introduction to Neural Networks falls into a new ecological niche for texts. Based on notes that have been class-tested for more than a decade, it is aimed at cognitive science and neuroscience students who need to understand brain function in terms of computational modeling, and at engineers who want to go beyond...
Society for Industrial and Applied Mathematics, 2001, -143 p. This short book considers selected topics involving the interplay between certain areas of discrete mathematics and the simplest types of artificial neural networks. Graph theory, some partially ordered set theory, computational complexity, and discrete probability theory are among the mathematical topics involved....
Cambridge University Press, 2009, 389 p. This important work describes recent theoretical advances in the study of artificial neural networks. It explores probabilistic models of supervised learning problems, and addresses the key statistical and computational questions. Chapters survey research on pattern classification with binary-output networks, including a discussion of...
Boca Raton: CRC Press, 2009. — 214 p. Recurrent High Order Neural Networks, RHONN Experimental Results: Identi cation of Costs Maps Using RHONNs Object Recognition and Geometric Representations of Objects Geometric Algebra: An Overview Clifford SVM Conformal Neuron and Hyper-Conformal Neuron Neural Identification Inverse Optimal Neural Control IONC for Non-Holonomic Mobile...
Springer Briefs in Applied Sciences and Technology: Computational Intelligence. — Springer, 2014. — 81 p. In this book, a novel optimization method inspired by a paradigm from nature is introduced. The chemical reactions are used as a paradigm to propose an optimization method that simulates these natural processes. The proposed algorithm is described in detail and then a set...
Academic Press, 2019. — 298 p. — ISBN: 978-0-12-811926-6. This book details the methodologies that are useful for keeping an ideal human-machine system up-to-date, along with information on how to prevent potential overload and minimize errors. It discusses neural measures and the proper methods and technologies to maximize performance, thus providing a resource for...
John Wiley, 2002. — 313 p. The definition of human intelligence has always been elusive. Despite years of research, scientists are still unable to describe accurately the reasoning process of the human brain. Scientific models have been used to describe the human reasoning process. Even though such models are never foolproof, they turn out to be suitable for most practical...
Birkhäuser, 1998. — 307 p. This book is devoted to a novel conceptual theoretical framework of neuro science and is an attempt to show that we can postulate a very small number of assumptions and utilize their heuristics to explain a very large spectrum of brain phenomena. The major assumption made in this book is that inborn and acquired neural automatisms are generated...
Nornwood: Intellect Ltd, 1994. — 511 p. Presenting research on the computational abilities of connectionist, neural, and neurally inspired systems, this series emphasizes the question of how connectionist or neural network models can be made to perform rapid, short-term types of computation that are useful in higher-level cognitive processes. The most recent volumes are...
Springer, 2015. — 392 p. This research book aims to provide the reader with a selection of high-quality papers devoted to current progress and recent advances in the now mature field of Artificial Neural Networks (ANN). Not only relatively novel models or modifications of current ones are presented, but many aspects of interest related to their architecture and design are...
Springer, 2015. — 392 p. This book collects research works that exploit neural networks and machine learning techniques from a multidisciplinary perspective. Subjects covered include theoretical, methodological and computational topics which are grouped together into chapters devoted to the discussion of novelties and innovations related to the field of Artificial Neural...
Springer, 2016. — 525 p. Advances in Neural Networks: Computational and Theoretical Issues is a book series dedicated to recent advances in computational and theoretical issues of artificial intelligence methods. Special attention is reserved to information communication technologies (ICT) applications that are of public utility and profitable for a living science that simplify...
Springer, 2017. — 122 p. Neuroscience is nowadays one of the most appealing research fields for interdisciplinary research. The rich dynamics and complexity of living neuronal networks, and the brain in particular, has long fascinated biologists, physicists and mathematicians alike. In the last decade, however, and thanks to the giant development in computational tools and...
Springer, 2022. — 361 p. — (Understanding Complex Systems). — ISBN: 978-3-030-91373-1. This book discusses its potential to model real-world systems and how considering their higher-order organization can lead to the emergence of novel dynamical behavior. Over the last decades, networks have emerged as the paradigmatic framework to model complex systems. Yet, as simple...
Idea Group, 2006. — 411 p. Studies into human movement sciences have been usually undertaken from an interdisciplinary perspective. Individuals and groups who are involved in movement science research come from a number of diverse backgrounds, including: biomechanics, biomedical engineering, health science, exercise science, sports science, computer science, clinical science,...
Springer, 2003, -244 p. It is my pleasure and privilege to write the foreword for this book, whose results I have been following and awaiting for the last few years. This monograph represents the outcome of an ambitious project oriented towards advancing our knowledge of the way the human visual system processes images, and about the way it combines high level hypotheses with...
Now Publishers, 2009. — 130. Can machine learning deliver AI? Theoretical results, inspiration from the brain and cognition, as well as machine learning experiments suggest that in order to learn the kind of complicated functions that can represent high-level abstractions (e.g. in vision, language, and other AI-level tasks), one would need deep architectures. Deep architectures...
Athena Scientific, 1996. — 505 p. This is the first textbook that fully explains the neuro-dynamic programming/reinforcement learning methodology, which is a recent breakthrough in the practical application of neural networks and dynamic programming to complex problems of planning, optimal decision making, and intelligent control. Neuro-dynamic programming uses neural network...
Singapore: Springer, 2017. - 267 p. This book focuses on neuro-engineering and neural computing, a multi-disciplinary field of research attracting considerable attention from engineers, neuroscientists, microbiologists and material scientists. It explores a range of topics concerning the design and development of innovative neural and brain interfacing technologies, as well as...
Springer, 2017. — 74 p. — (SpringerBriefs in Computer Science). — ISBN10: 3319703374, 13 978-3319703374. The key component in forecasting demand and consumption of resources in a supply network is an accurate prediction of real-valued time series. Indeed, both service interruptions and resource waste can be reduced with the implementation of an effective forecasting system....
Springer, 2009. — 296 p. This research book presents some of the most recent advances in neural information processing models including both theoretical concepts and practical applications. The contributions include: Advances in neural information processing paradigms Self organising structures Unsupervised and supervised learning of graph domains Neural grammar networks Model...
Oxford: Oxford University Press, 2018. — 417 p. Multilayer networks is a rising topic in Network Science which characterizes the structure and the function of complex systems formed by several interacting networks. Multilayer networks research has been propelled forward by the wide realm of applications in social, biological and infrastructure networks and the large...
New York: Springer, 2019. — 150 p. This book describes models of the neuron and multilayer neural structures, with a particular focus on mathematical models. It also discusses electronic circuits used as models of the neuron and the synapse, and analyses the relations between the circuits and mathematical models in detail. The first part describes the biological foundations and...
Oxford University Press, 1996. — 504 p. — ISBN10: 0198538642 Basic book for teaching neural networks in Western universities. This is the first comprehensive treatment of feed-forward neural networks from the perspective of statistical pattern recognition. After introducing the basic concepts, the book examines techniques for modeling probability density functions and the...
Wiley – Scrivener Publishing, 2017. — 303 p. — ISBN: 978-1-119-22354-2. Artificial neural network (ANN) has proven to be a universal approximator for any non-linear continuous function with arbitrary accuracy. This book presents how to apply ANN to measure various software reliability indicators: number of failures in a given time, time between successive failures, fault-prone...
Springer International Publishing AG, 2017. — 457 p. — (Texts in Applied Mathematics 66) — ISBN-10 331951170X. This book is intended as a text for a one-semester course on Mathematical and Computational Neuroscience for upper-level undergraduate and beginning graduate students of mathematics, the natural sciences, engineering, or computer science. An undergraduate introduction...
Springer International Publishing AG, 2017. — 445 p. — (Texts in Applied Mathematics 66) — ISBN10: 331951170X. This book is intended as a text for a one-semester course on Mathematical and Computational Neuroscience for upper-level undergraduate and beginning graduate students of mathematics, the natural sciences, engineering, or computer science. An undergraduate introduction...
Busharest: GAER Publishing House, 2020. — 84 p. This book covers in the first part the theoretical aspects of neural networks and their functionality, and then based on the discussed concepts it explains how to find-tune a neural network to yield highly accurate prediction results which are adaptable to any classification tasks. The introductory part is extremely beneficial to...
Springer, 1995, -296 p. This book is the result of a concerted action by the departments of Computer Science and Mathematics of the University of Limburg (Maastricht, The Netherlands) to develop a collection of lectures, specifically dedicated to informing the industrial world about the potential of using neural networks. For this reason, both departments had worked together...
Springer, 1995. — 295 p. This book presents carefully revised versions of tutorial lectures given during a School on Artificial Neural Networks for the industrial world held at the University of Limburg in Maastricht, Belgium. The major ANN architectures are discussed to show their powerful possibilities for empirical data analysis, particularly in situations where other...
CRC Press, 1997. — 278 p. — ISBN: 13: 978-0-7503-0499-3. Neural Network Analysis, Architectures, and Applications discusses the main areas of neural networks, with each authoritative chapter covering the latest information from different perspectives. Divided into three parts, the book first lays the groundwork for understanding and simplifying networks. It then describes novel...
We show that under suitable assumptions (primarily linearization) a simple and perspicuous online learning rule for Information Bottleneck optimization with spiking neurons can be derived. This rule performs on common benchmark tasks as well as a rather complex rule that has previously been proposed. Furthermore, the transparency of this new learning rule makes a theoretical...
Academic Press, 2016. — 484. Fundamentals of Brain Network Analysis is a comprehensive and accessible introduction to methods for unraveling the extraordinary complexity of neuronal connectivity. From the perspective of graph theory and network science, this book introduces, motivates and explains techniques for modeling brain networks as graphs of nodes connected by edges, and...
Singapore: World Scientific, 2019. — 418 p. Fault diagnosis is useful for technicians to detect, isolate, identify faults, and troubleshoot. Bayesian network (BN) is a probabilistic graphical model that effectively deals with various uncertainty problems. This model is increasingly utilized in fault diagnosis. This unique compendium presents bibliographical review on the use of...
Nova Science Pub Inc., NY, USA, 2017. — 243 p. — (Computer Science, Technology and Applications) — ISBN: 1634859642. This current book provides new research on artificial neural networks (ANNs). Topics discussed include the application of ANNs in chemistry and chemical engineering fields; the application of ANNs in the prediction of biodiesel fuel properties from fatty acid...
Independently published, 2023. – 80 p. Dive into the foundations of Artificial Intelligence, demystifying the core concept of Neural Networks. From the basics of neurons and synapses to the intricate architecture of these intelligent systems, each chapter unfolds the secrets behind the machines that can learn, adapt, and make decisions on their own. Learn the building blocks of...
Springer, 2010. - 617 p. The book shows a very original organization addressing in a non traditional way, but with a systematic approach, to who has an interest in using mathematics in the social sciences. The book is divided in four parts: (a) a historical part, written by Vittorio Capecchi which helps us understand the changes in the relationship between mathematics and...
InTech, 2011, -112 p. The RNNs (Recurrent Neural Networks) are a general case of artificial neural networks where the connections are not feed-forward ones only. In RNNs, connections between units form directed cycles, providing an implicit internal memory. Those RNNs are adapted to problems dealing with signals evolving through time. Their internal memory gives them the...
2nd edition. — Humana Press, 2015. — 353 p. — (Methods in Molecular Biology 1260). Artificial Neural Networks (ANNs) are among the most fundamental techniques within the field of Artificial Intelligence. Their operation loosely emulates the functioning of the human brain, but the value of an ANN extends well beyond its role as a biological model. An ANN can both memorize and...
3rd edition. — Humana Press, 2021. — 368 p. — ISBN: 978-1-0716-0825-8. This volume presents examples of how Artificial Neural Networks (ANNs) are applied in biological sciences and related areas. Chapters cover a wide variety of topics, including the analysis of intracellular sorting information, prediction of the behavior of bacterial communities, biometric authentication,...
World Scientific Publishing Co. Pte. Ltd., 2021. — 191 p. — ISBN: 978-981-123-020-2. The aim of this book is to handle different application problems of science and engineering using expert Artificial Neural Network (ANN). As such, the book starts with basics of ANN along with different mathematical preliminaries with respect to algebraic equations. Then it addresses ANN based...
Boca Raton: CRC Press, 2017. — 153 p. Differential equations play a vital role in the fields of engineering and science. Problems in engineering and science can be modeled using ordinary or partial differential equations. Analytical solutions of differential equations may not be obtained easily, so numerical methods have been developed to handle them. Machine intelligence...
Lawrence Erlbaum, 1995, -575 p. Almost ten years have passed since the publication of the now classic volumes Parallel Distributed Processing: Explorations in the Microstructure of Cognition. These volumes marked a renewal in the study of brain-inspired computations as models of human cognition. Since the publication of these two volumes, thousands of scientists and engineers...
Springer, 2011. — 487 p. — ISBN: 978-0-85729-096-0. The Boolean network has become a powerful tool for describing and simulating cellular networks in which the elements behave in an on–off fashion. Analysis and Control of Boolean Networks presents a systematic new approach to the investigation of Boolean control networks. The fundamental tool in this approach is a novel matrix...
CRC Press, 2011. — 257 p. Using real-life examples to illustrate the performance of learning algorithms and instructing readers how to apply them to practical applications, this work offers a comprehensive treatment of subspace learning algorithms for neural networks. The authors summarize a decade of high quality research offering a host of practical applications. They...
Ruhr-Universität Bochum, Germany, Cheng S., 2021. Artificial neural networks (ANN) were inspired by the architecture and function of the brain. Nevertheless, their greatest strength is not that they are good models of the brain, but rather that they are powerful function approximators. Since the 1980's many types of ANN have been developed and tricks for training ANNs on data...
Academic Press, 2019. — 369 p. — ISBN: 9780128177969. Source Separation and Machine Learning presents the fundamentals in adaptive learning algorithms for Blind Source Separation (BSS) and emphasizes the importance of machine learning perspectives. It illustrates how BSS problems are tackled through adaptive learning algorithms and model-based approaches using the latest...
Imperial College Press, 2007, -322 p. The area of Neural computing that we shall discuss in this book represents a combination of techniques of classical optimization, statistics, and information theory. Neural network was once widely called artificial neural networks, which represented how the emerging technology was related to artificial intelligence. It once was a topic that...
Cambridge University Press, 2004, -410 p. Cellular Nonlinear/Neural Network (CNN) technology is both a revolutionary concept and an experimentally proven new computing paradigm. Analogic cellular computers based on CNNs are set to change the way analog signals are processed. This unique undergraduate level textbook includes many examples and exercises, including CNN simulator...
Cambridge University Press, 2019. — 344 p. — ISBN: 978-1-107-18486-2. This tutorial reference serves as a coherent overview of various statistical and mathematical approaches used in brain network analysis, where modeling the complex structures and functions of the human brain often poses many unique computational and statistical challenges. This book fills a gap as a textbook...
John Wiley, 2010, -277 p. The history of data analysis can be divided into two main periods: before the 1970–1980 decades and after. Before the 1970–1980 decades it was mainly the domain of applied mathematics and statistics. During this period, researchers developed linear techniques (now defined as classical) under various predefined algorithms with some successful approaches...
MIT Press, 1999. — 499. Knowledge-based neurocomputing concerns the utilization of problem-specific knowledge within the neurocomputing paradigm. The novelty of this book is that it illustrates the use of explicit comprehensible knowledge, a feature not always available within artificial neural networks. We believe that the issue of explicit knowledge manipulation within the...
Oxford University Press, 2005, -586 p. The study of the principles behind information processing in complex networks of simple interacting decision-making units, be these units cells (‘neurons’) in brains or in other nervous tissue, or electronic processors (or even software) in artificial systems inspired by biological neural networks, is one of the few truly interdisciplinary...
Physica-Verlag, 2000. — 207. It is possible to design artificial systems to replace or "duplicate" the human expert. There are many possible definitions of intelligence systems. One of them is that: an intelligence system is a system able to make decisions that would be regarded as intelligent if they were observed in humans. Intelligence systems adapt themselves using some...
Springer, 2017. — 309 p. What are artificial neural networks? What is their purpose? What are their potential practical applications? What kind of problems can they solve? With these questions in mind, this book was written with the primary concern of answering readers with different profiles, from those interested in acquiring knowledge about architectures of artificial neural...
New York: Springer, 2017. — 303 p. This book provides comprehensive coverage of neural networks, their evolution, their structure, the problems they can solve, and their applications. The first half of the book looks at theoretical investigations on artificial neural networks and addresses the key architectures that are capable of implementation in various application...
Springer, 1994. — 474 p. The quest for building systems that can function automatically has attracted a lot of attention over the centuries and created continuous research activities. As users of these systems we have never been satisfied, and demand more from the artifacts that are designed and manufactured. The current trend is to build autonomous systems that can adapt to...
Basel, Switzerland: MDPI AG, 2016. — 260 p. — ISBN: 3038422703. Since their re-popularisation in the mid-1980s, artificial neural networks have seen an explosion of research across a diverse spectrum of areas. While an immense amount of research has been undertaken in artificial neural networks themselves - in terms of training, topologies, types, etc. - a similar amount of...
Packt Publishing, 2019. — 269 p. — ISBN: 978-1-78899-259-6. Design and create neural networks with deep learning and artificial intelligence principles using OpenAI Gym, TensorFlow, and Keras Neural networks play a very important role in deep learning and artificial intelligence (AI), with applications in a wide variety of domains, right from medical diagnosis, to financial...
New York: Springer, 2016. — x, 285 p. This book examines modern micro- and nanofabrication technologies to develop improved tools for stimulating and recording electrical activity in neuronal networks. It discusses the compatibility of fabrication techniques optimized for different devices.
Berlin: Walter de Gruyter, 2022. — 174 p. — (De Gruyter Series in Applied and Numerical Mathematics 6). — ISBN: 3110783126. This book uses numerical analysis as the main tool to investigate methods in machine learning and A.I. The efficiency of neural network representation for polynomial functions is studied in detail, together with an original description of the Latin...
De Gruyter, 2022. — 174 p. — (De Gruyter Series in Applied and Numerical Mathematics 06). — 978-3-11-078318-6. Artificial Intelligence, Deep Learning, Machine Learning - whatever you’re doing if you don’t understand it - learn it. Because otherwise, you’re going to be a dinosaur within 3 years. This book uses numerical analysis as the main tool to investigate methods in Machine...
Singapore: Springer, 2023. — 261 p. — ISBN13: 9789811955297. — ISBN10: 9811955298. This book investigates the disagreement behavior analysis problems for signed networks in the presence of both cooperative and antagonistic interactions among agents. Owing to the existing antagonistic interactions, signed networks exhibit a variety of disagreement behaviors subject to different...
Minneapolis: University of Minnesota Press, 2024. — 125 p. A critical examination of the figure of the neural network as it mediates neuroscientific and computational discourses and technical practices. Neural Networks propose to reconstruct situated practices, social histories, mediating techniques, and ontological assumptions that inform the computational project of the same...
New York, John Wiley & Sons, 1996, 272 p., Formats: DJVU, ISBN: 0471054364 Systematically explores the relationship between principal component analysis (PCA) and neural networks. Provides a synergistic examination of the mathematical, algorithmic, application and architectural aspects of principal component neural networks. Using a unified formulation, the authors present...
Packt Publishing, 2023. — 196 p. — ISBN: 978-1804617625. Neuro-symbolic AI offers the potential to create intelligent systems that possess both the reasoning capabilities of symbolic AI and the learning capabilities of neural networks. This book provides an overview of AI and its inner mechanics, covering both symbolic and neural network approaches. You'll begin by exploring...
Springer, 2008. — 174 p. The main problem addressed in this book came out during a Fulbright research fellowship stage at U.C. Berkeley (California, USA, 1996-1998). Then, I had the opportunity to work in the research group of Leon Chua on a subject called CNN (cellular neural/nonlinear network). The CNN, developed in the end of the 1980s was an important step ahead in getting...
Springer, 1995, -371 p. The first volume of the Physics of Neural Networks series. This book offers a multifaceted presentation of several main issues in the theory of neural networks that have recently witnessed considerable progress: statistics and dynamics of Hopfield-type nets with symmetric and asymmetric couplings, learning algorithms, temporal association, structured...
Springer, 1995, -354 p. The second volume of the Physics of Neural Networks series. Since the appearance of Vol. 1 of Models of Neural Networks in 1991, the theory of neural nets has focused on two paradigms: information coding through coherent firing of the neurons and functional feedback. Information coding through coherent neuronal firing exploits time as a cardinal degree...
Springer, 1994, -322 p. The third volume of the Physics of Neural Networks series. One of the most challenging and fascinating problems of the theory of neural nets is that of asymptotic behavior, of how a system behaves as time proceeds. This is of particular relevance to many practical applications. Here we focus on association, generalization, and representation. We turn to...
Springer, 2021. — 186 p. — (Studies in Fuzziness and Soft Computing, 408). — ISBN: 978-3-030-72279-1. The research presented in this book shows how combining deep neural networks with a special class of fuzzy logical rules and multi-criteria decision tools can make deep neural networks more interpretable – and even, in many cases, more efficient. Fuzzy logic together with...
American Psychological Association, 2002. — 208 p. This text presents a neurocognitive model of dreams that draws from empirical research to explain better the process of dreaming and the nature of dream content. Contemporary advances in neuroscience, dream content analysis, cognitive linguistics, statistics and computer software have made it possible to revitalize this area of...
World Scientific, 1994. — 159 p. The Ising Magnetic Systems Physics of the Spin Glass State Replica Method Replica Symmetry Breaking Physics of the Replica Symmetry Breaking Replica Symmetry Breaking Solution Near Tc Scaling in the Space of Spin Glass States Experiments Partial Annealing Statistical Models of Neural Networks The Hopfield Model Partial Annealing in Neural...
MIT Press, 1996. — 241 p. Artificial Neural Networks (ANNs) offer an efficient method for finding optimal cleanup strategies for hazardous plumes contaminating groundwater by allowing hydrologists to rapidly search through millions of possible strategies to find the most inexpensive and effective containment of contaminants and aquifer restoration. ANNs also provide a faster...
MIT Press, 2023. — 224 p. — ISBN: 978-0262374675. An insightful investigation into the mechanisms underlying the predictive functions of neural networks — and their ability to chart a new path for AI. Prediction is a cognitive advantage like few others, inherently linked to our ability to survive and thrive. Our brains are awash in signals that embody prediction. Can we extend...
Boston: The MIT Press, 2015. — 483 p. Emergence — the formation of global patterns from solely local interactions — is a frequent and fascinating theme in the scientific literature both popular and academic. In this book, Keith Downing undertakes a systematic investigation of the widespread (if often vague) claim that intelligence is an emergent phenomenon. Downing focuses on...
John Wiley & Sons, Inc., 2021. — 51 p. — ISBN: 978-1-119-87905-3. Digital transformation is all about applications and agility. Modern digital services are usually built from multiple applications — especially in this age of microservices — and both your staff and your network must be optimized for quickly creating and deploying new services, changing services at the drop of a...
Study guide / monograph Publisher: Springer, -516 p., November 9, 2010 ISBN10: 3642061877ISBN13: 978-3642061875 Neural networks represent a powerful data processing technique that has reached maturity and broad application. When clearly understood and appropriately used, they are a mandatory component in the toolbox of any engineer who wants make the best use of the available...
John Wiley, 2007, -288 p. This work grew out of two stimuli. One was a series of problems arising in remote sensing (the interpretation of multi-band satellite imagery) and the other was the lack of answers to some questions in the neural network literature. Typically, in remote sensing, reflected light from the earth’s surface is gathered by a sensing device and recorded on a...
Academic Press, 2021. — 310 p. — ISBN: 978-0-12-819740-0. State of the Art in Neural Networks and Their Applications presents the latest advances in artificial neural networks and their applications across a wide range of clinical diagnoses. Advances in the role of machine learning, artificial intelligence, deep learning, cognitive image processing, and suitable data analytics...
Academic Press/Elsevier, 2023. — 328 p. State-of-the-Art in Neural Networks and Their Applications, Volume Two presents the latest advances in artificial neural networks (ANN) and their applications across a wide range of clinical diagnoses. The book provides overviews and case studies of advances in the role of Machine Learning, Artificial Intelligence (AI), Deep Learning...
InTech, 2012, -302 p. The first section illustrates some general concepts of artificial neural networks, their properties, mode of training, static training (feedforward) and dynamic training (recurrent), training data classification, supervised, semi-supervised and unsupervised training. Recurrent Neural Networks (RNNs), are like other ANN abstractions of biological nervous...
Time underlies many interesting human behaviors. Thus, the question of how to represent time in connectionist models is very important. One approach is to represent time implicitly by its effects on processing rather than explicitly (as in a spatial representation). The current report develops a proposal along these lines first described by Jordan (1986) which involves the use...
Academic Press; Elsevier, 2022. — 290 p. — ISBN: 978-0-12-820793-2. Artificial Neural Networks for Renewable Energy Systems and Real-World Applications present current trends for the solution of complex engineering problems in the application, modeling, analysis, and optimization of different energy systems and manufacturing processes. With growing research catering to the...
Apress Media, LLC., 2023. — 223 p. — ISBN-13: 978-1-4842-8998-3. Demystify the creation of efficient AI systems using the model-based reinforcement learning Unity ML-Agents - a powerful bridge between the world of Unity and Python. We will start with an introduction to the field of AI, then discuss the progression of AI and where we are today. We will follow this up with a...
Apress Media, LLC., 2023. — 223 p. — ISBN-13 978-1-4842-8997-6. Demystify the creation of efficient AI systems using the model-based reinforcement learning Unity ML-Agents - a powerful bridge between the world of Unity and Python. We will start with an introduction to the field of AI, then discuss the progression of AI and where we are today. We will follow this up with a...
Princeton University Press, 2005. — 266 p. The aim of this book is to explore the potential of neural network models to explain animal behavior at the motivational, ontogenetic and evolutionary levels. Our focus is on understanding behavior based on principles of operation of nervous systems. Ethology, psychology and behavioral ecology have produced a large body of theory of...
Springer, 2018. — 380 p. This book presents a collection of contributions in the field of Artificial Neural Networks (ANNs). The themes addressed are multidisciplinary in nature, and closely connected in their ultimate aim to identify features from dynamic realistic signal exchanges and invariant machine representations that can be exploited to improve the quality of life of...
Springer, 2018. — 313 p. — (Smart Innovation, Systems and Technologies). — ISBN10: 3319950975, 13 978-3319950976. This book proposes neural networks algorithms and advanced machine learning techniques for processing nonlinear dynamic signals such as audio, speech, financial signals, feedback loops, waveform generation, filtering, equalization, signals from arrays of sensors,...
Springer, 2018. — 544 p. — (Smart Innovation, Systems and Technologies). — ISBN10: 3319950975, 13 978-3319950976. This book proposes neural networks algorithms and advanced machine learning techniques for processing nonlinear dynamic signals such as audio, speech, financial signals, feedback loops, waveform generation, filtering, equalization, signals from arrays of sensors,...
Singapore: Springer, 2020. — 523 p. The book presents research that contributes to the development of intelligent dialog systems to simplify diverse aspects of everyday life, such as medical diagnosis and entertainment. Covering major thematic areas: machine learning and artificial neural networks; algorithms and models; and social and biometric data for applications in...
Pearson, 1993, -469 p. There has been a resurgence of interest in artificial neural networks over the last few years, as researchers from diverse backgrounds have produced a firm theoretical foundation and demonstrated numerous applications of this rich field of study. However, the interdisciplinary nature of neural networks complicates the development of a comprehensive, but...
Pearson, 1993, -469 p. There has been a resurgence of interest in artificial neural networks over the last few years, as researchers from diverse backgrounds have produced a firm theoretical foundation and demonstrated numerous applications of this rich field of study. However, the interdisciplinary nature of neural networks complicates the development of a comprehensive, but...
Independently published, 2024. — 81 p. — ISBN-13 979-8327132856. "Neural Networks for Beginners: Unlock the Secrets of Neural Networks" is your essential guide to understanding and mastering one of artificial intelligence's most powerful tools. Whether you're new to the world of neural networks or looking to deepen your understanding, this beginner-friendly book demystifies...
Springer, 2022. — 435 p. — ISBN: 978-3-031-01232-7. This open-access book brings together the latest developments from industry and research on automated driving and artificial intelligence. Environment perception for highly automated driving heavily employs deep neural networks, facing many challenges. How much data do we need for training and testing? How to use synthetic...
Nova Science Publishers, 2011, -425 p. This book gathers the most current research from across the globe in the study of artificial neural networks. Topics discussed include artificial neural networks in environmental sciences and chemical engineering; application of artificial neural networks in the development of pharamceutical microemulsions; massive-training artificial...
World Scientific, 1998. -316 p. The field of artificial neural networks has come a long way. Depending on one’s perspective, one can trace its origins to the research on Mach bands in visual perception and its interpretation in terms of lateral inhibition, to Hodgkin and Huxley’s transmission line model of a nerve fiber, to McCulloch and Pitt’s model of a neuron, to Hebb’s...
Continuum, 2009. — 245 p. This is an engaging study of the mental lexicon: the way in which the form and meaning of words is stored by speakers of specific languages. Fortescue attempts to narrow the gap between the results of experimental neurology and the concerns of theoretical linguistics in the area of lexical semantics. The prime goal as regards linguistic theory is to...
Addison-Wesley Publishing Company, 1991 The material from which this book was written was originally developed for a series of short courses and seminars for practicing engineers. For many of our students, the courses provided a first exposure to the technology. Some were computer-science majors with specialties in artificial intelligence, but many came from a variety of...
Department of Computing and Information Systems, The University of Paisley, 1996, -136 p. This course is an introduction to the subject of Artificial Neural Networks and Genetic Algorithms, two very new subjects forming part of Distributed Artificial Intelligence. As you leaf through these notes you will notice that they are full of mathematical equations. The reason is simple:...
University of Paisley, 2000, -204 p. This course comprises an advanced course to those new information processing simulations which are intended to emulate the information processors which we find in biology. Traditional artificial intelligence is based on high-level symbol processing i.e. logic programming, expert systems, semantic nets etc. all rely on there being in...
Neural Network Learning and Expert Systems presents a unified and in-depth development of neural network learning algorithms and neural network expert systems. Especially suitable for students and researchers in computer science, engineering, and psychology, it provides a systematic development of neural network learning algorithms from a computational perspective. This is...
Springer, 2007, -402 p. Professor A. I. Galushkin’s monograph Neural Networks Theory appears at a time when the theory has achieved maturity and is the fulcrum of a vast literature. Nevertheless, Professor Galushkin’s work has high importance because it serves a special purpose which is explained in the following. The roots of neural networks theory go back to the pioneering...
Springer, 2003. — 180 p. The main goal of this book is to present recent results in the area of feedback linearisation using empirical models based on dynamic neural networks and to provide the reader with methods for analysing, designing and implementing these techniques. The field of neural computations has evolved from neurological roots when the first artificial neural...
Springer, 2016. — 111. In this book, a neural network learning method with type-2 fuzzy weight adjustment is proposed. The mathematical analysis of the proposed learning method architecture and the adaptation of type-2 fuzzy weights are presented. The proposed method is based on research of recent methods that handle weight adaptation and especially fuzzy weights. The internal...
Springer, 2010. — 298 p. This book introduces the novel concept of a fuzzy network. In particular, it describes further developments of some results from its predecessor book on Complexity Management in Fuzzy Systems, published in 2007 in the Springer Series in Studies in Fuzziness and Soft Computing. The book contents build on a number of special presentations made by the...
Cambridge: Cambridge University Press, 2002. — 496 p. — ISBN 978-0-521-81384-6. This introduction to spiking neurons can be used in advanced-level courses in computational neuroscience, theoretical biology, neural modeling, biophysics, or neural networks. It focuses on phenomenological approaches rather than detailed models in order to provide the reader with a conceptual...
Nova Science Publishers, 2024. — 199 p. — (Computer Science, Technology and Applications). — eBook ISBN: 979-8-89113-725-7. This book is a compilation of eleven quality articles exploring a variety of aspects of applications of ANN. Various authors of the articles from India and abroad have presented their work around the applications of ANN in healthcare and self-medication...
Amazon Digital Services LLC, 2018. — 128 p. — ASIN B07GF5KP9R This book is an introduction to Intent Based Networking and how your business can leverage many of the benefits that it realises through network modernisation, optimisation and business alignment. Organisations around the world of all sizes are having to adjust to doing business globally. But one of the biggest...
The lecture was given at the USA-NIS Neurocomputing Opportunities workshop, Washington DC, in July 1999 (Associated with IJCNN'99). What is neuroinformatics? We can define it as a direction of science and information technology, dealing with the development and study of the methods for the solution of problems using neural networks. A field of science cannot be determined only...
Springer, 2002. — 367. Traditional Artificial Intelligence (AI) systems adopted symbolic processing as their main paradigm. Symbolic AI systems have proved effective in handling problems characterized by exact and complete knowledge representation. Unfortunately, these systems have very little power in dealing with imprecise, uncertain and incomplete data and information which...
Wiley & Sons, Inc., 2020. — 240 p. — ISBN: 978-1-119-62058-7. Keeping in view the growth of the technological frontiers, there is always a need for the development of reliable, fault tolerant and cost- effective interconnection networks (INs) which are the critical metrics to achieve the goal of parallelism. The main objective of this book is to design new fault tolerant...
3rd Edition. — World Scientific, 2013. — 363 p. — ISBN: 978-9814522731. Artificial neural networks are most suitable for solving problems that are complex, ill-defined, highly nonlinear, of many and different variables, and/or stochastic. Such problems are abundant in medicine, in finance, in security and beyond. This volume covers the basic theory and architecture of the major...
Springer, 2019. — 295 p. — ISBN: 9783030021610. This book covers the fundamental principles, new theories and methodologies, and potential applications of hybrid intelligent networks. Chapters focus on hybrid neural networks and networked multi-agent networks, including their communication, control and optimization synthesis. Hybrid Intelligent Networks Delayed Hybrid Impulsive...
Springer International Publishing AG, 2017. — 90 p. — ISBN: 978-3-319-67770-5. The authors discuss in the first part of this book, the semantic aspects of spatio-temporal graph data in two application domains, viz., urban transportation and social networks. Then the authors present representational models and data structures, which can effectively capture these semantics, while...
Springer International Publishing AG, 2017. — 103 p. — ISBN: 978-3-319-67770-5. The authors discuss in the first part of this book, the semantic aspects of spatio-temporal graph data in two application domains, viz., urban transportation and social networks. Then the authors present representational models and data structures, which can effectively capture these semantics,...
IEEE Press/John Wiley, 2003, -751 p. The human cognitive faculty - the carbon-based computer - has a vast network of processing cells called neural networks, and this science of neural networks has inspired many researchers in biological as well as nonbiological fields. This inspiration has generated keen interest among engineers, computer scientists, and mathematicians for...
IEEE Press/John Wiley, 2003, -751 p. The human cognitive faculty - the carbon-based computer - has a vast network of processing cells called neural networks, and this science of neural networks has inspired many researchers in biological as well as nonbiological fields. This inspiration has generated keen interest among engineers, computer scientists, and mathematicians for...
Bentham Science Publishers, December 19, 2023. — 238 p. — ISBN: 978-981-5165-36-4. This textbook provides a quick and easy understanding of multistage interconnection networks (MINs) for engineers. The book's contents focus on the design, performance metrics, and evaluation of these networks which are crucial in modern computer architecture. The contents equip engineering...
Routledge, 2003. — 148 p. This book grew out of a set of course notes for a neural networks module given as part of a Masters degree in “Intelligent Systems”. The people on this course came from a wide variety of intellectual backgrounds (from philosophy, through psychology to computer science and engineering) and I knew that I could not count on their being able to come to...
New York: Martin T. Hagan and Howard B. Demuth, 2014. - 1012 p. This book, by the authors of the Neural Network Toolbox for MatLAB, provides a clear and detailed coverage of fundamental neural network architectures and learning rules. In it, the authors emphasize a coherent presentation of the principal neural networks, methods for training them and their applications to...
Springer, 2000. — 155 p. A challenging question in machine learning is the following task: Is it possible to combine symbolic and connectionistic systems in some mechanism such that it contains the benefits of both approaches? A satisfying answer to this question does not exist up to now. However, approaches which tackle small parts of the problem exist. This monograph...
Springer, 2000. — 155 p. A challenging question in machine learning is the following task: Is it possible to combine symbolic and connectionistic systems in some mechanism such that it contains the benefits of both approaches? A satisfying answer to this question does not exist up to now. However, approaches which tackle small parts of the problem exist. This monograph...
Springer, 2000. — 155 p. Cellular neural networks (CNNs) constitute a class of recurrent and locally coupled arrays of identical dynamical systems (cells). The underlying equation governing the dynamics of each cell is nonlinear and the cells are assumed to operate in parallel. The connectivity among the cells is determined by a set of parameters denoted as a template set. A...
CRC Press, 2011, -210 p. The sudden rise in popularity of artificial neural networks (ANNs) during the 1980s and 1990s indicates that these techniques are efficient in solving complex chemical and biological problems. This is due to characteristics such as robustness, fault tolerance, adaptive learning, and massively parallel analysis capabilities. ANNs have been featured in a...
Prentice-Hall, 1994, 197 p., ISBN: 0-13-112194-4 N eural ne tworks is a subject lying at the inte rsection of psychology, mathe matics, n uroscience, and syste ms theory. Curre ntly this field is e xperiencing rapid deve lopment because of its applications. The applications include robotics, pattern recognition (for speech and vision systems), and understanding human brain-mind...
Singapore: World Scientific Publishing Co., 2014. — 200 p. — ISBN: 978-981-4618-61-8. This unique volume presents a fruitful and beautiful mathematical world hidden in Caianiello's neuronic equations, which describe the instantaneous behavior of a model of a brain or thinking machine. The detailed analysis from a viewpoint of dynamical systems , even in a single neuron case,...
Nature has developed methods for controlling the movements of organisms with many degrees of freedom which differ strongly from existing approaches for balance control in humanoid robots: Biological organisms employ kinematic synergies that simultaneously engage many joints, and which are apparently designed in such a way that their superposition is approximately linear. We...
2001. – 284 p. This self-contained book, consisting of seven chaplers. is devoted lo Kalman filter theory applied to the training and use of neural networks, and some applications of learning algorithms derived in this way. It is organized as follows: Chapter 1 presents an inlroductory treatment of Kalman filters, with emphasis on basic Kalman filter theory, the...
Third Edition. — Pearson Education, 2009, — 937 p. Neural Networks and Learning Machines, Third Edition is renowned for its thoroughness and readability. This well-organized and completely up-to-date text remains the most comprehensive treatment of neural networks from an engineering perspective. This is ideal for professional engineers and research scientists. Refocused,...
Prentice Hall, 1999. — 864 p. Neural Networks, or artificial neural networks to be more precise, represent a technology that is rooted in many disciphnes: neurosciences, mathematics, statistics, physics, computer science, and engineering. Neural networks find applications in such diverse fields as modeling, time series analysis, pattern recognition, signal processing, and...
Second Edition. — Prentice-Hall Publishing, 1999, -874 p. Neural Networks, or artificial neural networks to be more precise, represent a technology that is rooted in many disciphnes: neurosciences, mathematics, statistics, physics, computer science, and engineering. Neural networks find applications in such diverse fields as modeling, time series analysis, pattern recognition,...
3rd Edition. — Prentice Hall, 2009. — 906 p. For graduate-level neural network courses offered in the departments of Computer Engineering, Electrical Engineering, and Computer Science. Neural Networks and Learning Machines, Third Edition is renowned for its thoroughness and readability. This well-organized and completely up-to-date text remains the most comprehensive treatment...
ArXiv, 2021. — 569 p. Deep learning is a group of exciting new technologies for neural networks. Through a combination of advanced training techniques and neural network architectural components, it is now possible to create neural networks that can handle tabular data, images, text, and audio as both input and output. Deep learning allows a neural network to learn hierarchies...
CreateSpace Independent Publishing, 2015. — 374 p. — ISBN: 1505714346, 978-1505714340. Neural networks have been a mainstay of artificial intelligence since its earliest days. Now, exciting new technologies such as deep learning and convolution are taking neural networks in bold new directions. In this book, we will demonstrate the neural networks in a variety of real-world...
2nd Ed. - Heaton Research, Inc., 2008. - 428 p., ISBN10: 1604390093, ISBN13: 978-1604390094 Introduces the C# programmer to the world of Neural Networks and Artificial Intelligence. Neural network architectures, such as the feedforward, Hopfield, and self-organizing map architectures are discussed. Training techniques, such as backpropagation, genetic algorithms and simulated...
Heaton Research, 2016. — 102 p. This book introduces the reader to the basic math used for neural network calculation. This book assumes the reader has only knowledge of college algebra and computer programming. This book begins by showing how to calculate output of a neural network and moves on to more advanced training methods such as backpropagation, resilient propagation...
2nd Edition. - Heaton Research, Inc., 2008. - 440 p., ISBN: 1604390085 Introduction to Neural Networks in Java introduces the Java programmer to the world of Neural Networks and Artificial Intelligence. Neural network architectures such as the feedforward backpropagation, Hopfield, and Kohonen networks are discussed. Additional AI topics, such as Genetic Algorithms and...
Heaton Research, Inc., 2005. - 380 p., ISBN: 097732060X Introduction to Neural Networks with Java, Second Edition, introduces the Java programmer to the world of Neural Networks and Artificial Intelligence. Neural network architectures, such as the feedforward, Hopfield, and self-organizing map architectures are discussed. Training techniques, such as backpropagation, genetic...
St. Louis, MO USA, Heaton Research, Inc., 2010. — 480 p. — ISBN: 9781604390070. Encog is an advanced neural network and bot programming framework. This book focuses on using Encog to create a variety of neural network architectures using the Java programming language. Neural network architectures such as feedforward/perceptrons, Hopfield, Elman, Jordan, Radial Basis Function,...
Springer, 2020. - 220 p. - ISBN: 3030464431 This book presents a self-contained introduction to techniques from field theory applied to stochastic and collective dynamics in neuronal networks. These powerful analytical techniques, which are well established in other fields of physics, are the basis of current developments and offer solutions to pressing open problems in...
Cybellium Ltd, 2023-12-02. — 323 p. — ASIN: B0CPD5FQTV. Unleash the Power of Deep Learning for Intelligent Systems. In the realm of artificial intelligence and machine learning, neural networks stand as the driving force behind intelligent systems that mimic human cognition. "Mastering Neural Networks" is your ultimate guide to comprehending and harnessing the potential of...
Addison-Wesley, 1991, -338 p. This book comprehensively discusses the neural network models from a statistical mechanics perspective. It starts with one of the most influential developments in the theory of neural networks: Hopfield's analysis of networks with symmetric connections using the spin system approach and using the notion of an energy function from physics....
Princeton University Press, 2021. — 368 p. What neurobiology and artificial intelligence tell us about how the brain builds itself. How does a neural network become a brain? While neurobiologists investigate how nature accomplishes this feat, computer scientists interested in artificial intelligence strive to achieve this through technology. The Self-Assembling Brain tells the...
Princeton University Press, 2021. — 368 p. — ISBN: 978-0691181226. What neurobiology and artificial intelligence tell us about how the brain builds itself How does a neural network become a brain? While neurobiologists investigate how nature accomplishes this feat, computer scientists interested in artificial intelligence strive to achieve this through technology. The...
MIT Press, 1991. — 264 p. Connectionist networks are composed of relatively simple, neuron-like processing elements that store all their long-term knowledge in the strengths of the connections between processors. In the last decade there has been considerable progress in developing learning procedures for these networks that allow them to automatically construct their own...
IEEE/John Wiley, 2013, -303 p. Complex-valued neural networks (CVNNs) have continued to open doors to various new applications. The CVNNs are the neural networks that deal with complex amplitude, i.e. signal having phase and amplitude, which is one of the most core concepts in science and technology, in particular in electrical and electronic engineering. A CVNN is not...
2nd ed. — Springer, 2012. — 198 p. — ISBN: 978-3642276316 This book is the second enlarged and revised edition of the first successful monograph on complex-valued neural networks (CVNNs) published in 2006, which lends itself to graduate and undergraduate courses in electrical engineering, informatics, control engineering, mechanics, robotics, bioengineering, and other relevant...
Universita degli studi di Verona. - March 2019 Problem overview Financial models Calibration problem Arti cal neural networks Dataset description Model interpretation Training and evaluation Souce code for stochastic process simulation Souce code for option valuation Souce code for neural networks
CRC Press, 2023. — 553 p. This book introduces artificial neural network (ANN)-based Lagrange optimization techniques for a structural design of prestressed concrete structures based on Eurocode 2, and composite structures based on American Institute of Steel Construction and American Concrete Institute standards. The book provides robust design charts for prestressed concrete...
Publication details not specified. The Neural Network Toolbox is written so that if you read Chapter 2, Chapter 3 and Chapter 4 you can proceed to a later chapter, read it and use its functions without difficulty. To make this possible, Chapter 2 presents the fundamentals of the neuron model, the architectures of neural networks. It also will discuss notation used in the...
ANN Book, 1998, -392 p. In recent years artificial neural networks (ANN) have emerged as a mature and viable framework with many applications in various areas. ANN are mostly applicable wherever some hard to define (exactly) patterns have to be dealt with. "Patterns" are taken here in the broadest sense, applications and models have been developed from speech recognition to...
Cambridge University Press, 2009, -365 p. Machine learning methods, having originated from computational intelligence (i.e. artificial intelligence), are now ubiquitous in the environmental sciences. This is the first single-authored textbook to give a unified treatment of machine learning methods and their applications in the environmental sciences. Machine learning methods...
InTech, 2008, -410 p. The research of neural networks has experienced several ups and downs in the 20th century. The last resurgence is believed to be initiated by several seminal works of Hopfield and Tank in the 1980s, and this upsurge has persisted for three decades. The Hopfield neural networks, either discrete type or continuous type, are actually recurrent neural networks...
Apress, 2024. — 173 p. — ISBN-13 979-8-8688-1019-0. Explore the capabilities of Machine Learning and neural networks. This comprehensive guidebook is tailored for professional programmers seeking to deepen their understanding of neural networks, Machine Learning (ML) techniques, and large language models (LLMs). The book explores the core of Machine Learning techniques,...
Apress Media, 2025. — 191 p. — ISBN-13: 979-8-8688-1020-6. Explore the capabilities of Machine Learning and neural networks. This comprehensive guidebook is tailored for professional programmers seeking to deepen their understanding of neural networks, Machine Learning (ML) techniques, and large language models (LLMs). The book explores the core of Machine Learning techniques,...
New York: Springer, 2022. — 302 p. This book highlights a comprehensive introduction to the fundamental statistical mechanics underneath the inner workings of neural networks. The book discusses in detail important concepts and techniques including the cavity method, the mean-field theory, replica techniques, the Nishimori condition, variational methods, the dynamical...
IEEE Press, 1999. — 315 p. The purpose of this book is to cover a broad range of topics relevant to computerassisted techniques for biomedical decision making. The book consists of three parts: neural networks, artificial intelligence, and alternative approaches. Part I provides a basis for understanding the theoretical and practical approaches to the development of neural...
ITexLi, 2023. — 136 p. — ISBN: 1837682224 9781837682225 1837699941 9781837699940 1837682232 9781837682232. This book examines artificial neural networks (ANNs) and their applications in various fields. Chapters address ANNs and deep learning techniques for real-world applications in health care, materials processing, energy management, and more. Artificial Intelligence at the...
New York: Springer, 2016. — 213 p. This book highlights the ability of neural networks (NNs) to be excellent pattern matchers and their importance in information retrieval (IR), which is based on index term matching. The book defines a new NN-based method for learning image similarity and describes how to use fuzzy Gaussian neural networks to predict personality. It introduces...
Springer, 2018. — 245 p. — ISBN: 978-9811301995. This book provides theoretical and practical knowledge about a methodology for evolutionary algorithm-based search strategy with the integration of several machine learning and deep learning techniques. These include convolutional neural networks, Gröbner bases, relevance vector machines, transfer learning, bagging and boosting...
Springer, 2018. — 245 p. — ISBN: 978-9811301995. This book provides theoretical and practical knowledge about a methodology for evolutionary algorithm-based search strategy with the integration of several machine learning and deep learning techniques. These include convolutional neural networks, Gröbner bases, relevance vector machines, transfer learning, bagging and boosting...
Springer, 2021. — 545 p. — ISBN: 978-3-030-80567-8. This book contains the proceedings of the 22nd EANN “Engineering Applications of Neural Networks” 2021 that comprises research papers on both theoretical foundations and cutting-edge applications of artificial intelligence. Based on the discussed research areas, emphasis is given to advances in machine learning (ML) focusing...
New York: Springer, 2016. - 119 p. New and practical approach in designing a computationally creative system that generates creative outputBreakthrough in automatic chess problem composing technology, challenging the prevailing dominance of human composers Offers a detailed explanation of the DSNS concept, the composing algorithm and composing source code This book describes a...
Providence: AMS, 2021. — 199 p. Recent years have witnessed a growth of interest in the special functions called ridge functions. These functions appear in various fields and under various guises. They appear in partial differential equations (where they are called plane waves), in computerized tomography, and statistics. Ridge functions are also the underpinnings of many...
Authors: Lakhmi C. Jain (Editor), V. Rao Vemuri (Editor) Publisher: CRC Press Year: 1998 Pages: 297 Industrial Applications of Neural Networks explores the success of neural networks in different areas of engineering endeavors. Each chapter shows how the power of neural networks can be exploited in modern engineering applications. The first seven chapters focus on image...
CRC Press, 2000, -358 p. Neural networks are a new generation of information processing paradigms designed to mimic some of the behaviors of the human brain. These networks have gained tremendous popularity due to their ability to learn, recall and generalize from training data. A number of neural network paradigms have been reported in the last four decades, and in the last...
Springer, 2000. — 267 p. At the dawn of the new millennium, the design and application of artificial neural networks continues to be the subject of intense research both in industry and in universities. In the last two decades, artificial neural networks have been refined and widely used by researchers and application engineers. The Adaptive Resonance Theory (ART) neural...
Springer, 2005. — 208 p. The identification of nonlinear systems using the block-oriented approach has been developed since the half of 1960s. A large amount of knowledge on this subject has been accumulated through literature. However, publications are scattered over many papers and there is no book which presents the subject in a unified framework. This has created an...
Prentice-Hall, 1997. — 640 p. Neuro-Fuzzy Modeling and Soft Computing places particular emphasis on the theoretical aspects of covered methodologies, as well as empirical observations and verifications of various applications in practice. Neuro-Fuzzy Modeling and Soft Computing is oriented toward methodologies that are likely to be of practical use. It includes exercises, some...
Birkhäuser, 1991, -176 p. In mathematics there are limits, speed limits of a sort, on how many computational steps are required to solve certain problems. The theory of computational complexity deals with such limits, in particular whether solving an n-dimensional version of a particular problem can be accomplished with, say, n2 steps or will inevitably require 2n steps. Such a...
MIT Press, 2001. — 433 p. Probabilistic Independence Networks for Hidden Markov Probability Models. Learning and Relearning in Boltzmann Machines. Learning in Boltzmann Trees. Deterministic Boltzmann Learning Performs Steepest Descent in Weight-Space. Attractor Dynamics in Feedforward Neural Networks. Efficient Learning in Boltzmann Machines Using Linear Response Theory....
EHGBooks, 2018. — 397 p. — ASIN B07FHYXDYG. This book provides frequently studied and used machines together with soft computing methods such as evolutionary computation. The main topics of the machine learning cover Artificial Neural Networks (ANNs), Radial Basis Function Networks (RBFNs), Fuzzy Neural Networks (FNNs), Support Vector Machines (SVMs), and Wilcoxon Learning...
EHGBooks, 2018. — 397 p. — ASIN B07FHYXDYG. This book provides frequently studied and used machines together with soft computing methods such as evolutionary computation. The main topics of the machine learning cover Artificial Neural Networks (ANNs), Radial Basis Function Networks (RBFNs), Fuzzy Neural Networks (FNNs), Support Vector Machines (SVMs), and Wilcoxon Learning...
The MIT Press, 2020. — 271 p. — ISBN: 978-0262044615. An up-to-date overview of the field of connectomics, introducing concepts and mechanisms underlying brain network change at different stages. The human brain undergoes massive changes during its development, from early childhood and the teenage years to adulthood and old age. Across a wide range of species, from C. elegans...
Idea Group Publishing, 2006, -299 p. Artificial neural networks (ANNs) have attracted increasing attentions in recent years for solving many real-world problems. ANNs have been successfully applied in solving many complex problems where traditional problem-solving methods have failed or proved insufficient. With significant advances in processing power, neural networks research...
New York: Springer, 2019. — 738 p. — (Springer Series on Bio- and Neurosystems, vol. 7). — ISBN: 978-3-662-57715-8. Spiking neural networks (SNN) are biologically inspired computational models that represent and process information internally as trains of spikes. This monograph book presents the classical theory and applications of SNN, including original author’s contribution...
Second printing, 1998 1996 Massachusetts Institute of Technology This book solves the problem of designing computerized reasoning, by combining the three techniques to minimize their weaknesses and enhance their strong points. The book begins with an excellent introduction to AI, fuzzy-, and neuroengineering. The author succeeds in explaining the fundamental ideas and practical...
Hauppauge: Nova Science Publishers, Incorporated, 2011. — 68 p. Artificial neural networks (ANN) are widely used in diverse fields of science and industry. Though there have been numerous techniques used for their implementations, the choice of a specific implementation is subject to different factors including cost, accuracy, processing speed, and overall performance. Featured...
Butterworth-Heinemann, 2016. — 254 p. This book presents the basics of FNNs, in particular T2FNNs, for the identification and learning control of real-time systems. In addition to conventional parameter tuning methods, e.g., GD, SMC theory-based learning algorithms, which are simple and have closed forms, their stability analysis are also introduced. This book has been prepared...
New York: Springer, 2018. — 142 p. This book presents recent research on the evolution of artificial neural development, and searches for learning genes. It is fascinating to see how all biological cells share virtually the same traits, but humans have a decided edge over other species when it comes to intelligence. Although DNA decides the form each particular species takes,...
New York: Morgan & Claypool, 2018. — 187 p. A Guide to Convolutional Neural Networks for Computer Vision Synthesis Lectures on Computer Vision February 2018, 207 p. Computer vision has become increasingly important and effective in recent years due to its wide-ranging applications in areas as diverse as smart surveillance and monitoring, health and medicine, sports and...
Springer, 2007. — 249 p. Artificial neural networks (ANNs), which are parallel computational models, comprising of interconnected adaptive processing units (neurons) have the capability to predict accurately the dispersive behavior of vehicular pollutants under complex environmental conditions. This book aims at describing step-by-step procedure for formulation and development...
New York: Engineering Science Reference, IGI Global, 2022. — 1612 p. Artificial neural networks (ANNs) present many benefits in analyzing complex data in a proficient manner. As an effective and efficient problem-solving method, ANNs are incredibly useful in many different fields. From education to medicine and banking to engineering, artificial neural networks are a growing...
Information Science Reference, 2014. — 524 p. — (Advances in Wireless Technologies and Telecommunication). — ISBN: 1466660503, 9781466660502 The use of game theoretic techniques is playing an increasingly important role in the network design domain. Understanding the background, concepts, and principles in using game theory approaches is necessary for engineers in network...
The extraction of statistically independent components from high-dimensional multi-sensory input streams is assumed to be an essential component of sensory processing in the brain. Such independent component analysis (or blind source separation) could provide a less redundant representation of information about the external world. Another powerful processing strategy is to...
It is open how neurons in the brain are able to learn without supervision to discriminate between spatio-temporal firing patterns of presynaptic neurons. We show that a known unsupervised learning algorithm, Slow Feature Analysis (SFA), is able to acquire the classification capability of Fisher’s Linear Discriminant (FLD), a powerful algorithm for supervised learning, if...
Wiesbaden: Springer Vieweg, 2022. — 89 p. Multilayer neural networks based on multi-valued neurons (MLMVNs) have been proposed to combine the advantages of complex-valued neural networks with a plain derivative-free learning algorithm. In addition, multi-valued neurons (MVNs) offer a multi-valued threshold logic resulting in the ability to replace multiple conventional output...
Unigrafia, 2014. — 201 p. The method discussed here, the Self-Organizing Map (SOM) introduced by the author, is a data-analysis method. It produces low-dimensional projection images of high-dimensional data distributions, in which the similarity relations between the data items are preserved. In other words, it is able to cluster the data, but at the same it orders the...
Springer, 2009. — 108 p. Artificial neural networks (ANNs) serve as powerful computational tools in a diversity of applications including: classification, pattern recognition, function approximation, and the modeling of biological neural networks. Equipped with procedures for learning from examples, ANNs can solve problems for which no algorithmic solution is known. A major...
Springer, 2017. — 334 p. — ISBN: 978-981-10-2913-4. This book not only provides a comprehensive introduction to neural-based PCA methods in control science, but also presents many novel PCA algorithms and their extensions and generalizations, e.g., dual purpose, coupled PCA, GED, neural based SVD algorithms, etc. It also discusses in detail various analysis methods for the...
Packt Publishing Ltd., 2018. — 115 p. — ISBN: 978-1-78913-233-5. Learn how to develop intelligent applications with sequential learning and apply modern methods for language modeling with neural network architectures for deep learning with Python’s most popular TensorFlow framework. Developers struggle to find an easy-to-follow learning resource for implementing Recurrent...
World Scientific Publishing, 2024. — 230 p. This book is a comprehensive step-by-step guide to data analysis and statistical analysis. It covers fundamental operations in Excel, such as table components, formula bars, and ribbons, and introduces visualization techniques and PDE derivation using Excel. It also provides an overview of Google Colab, including code and text cells,...
Academic Press, 2019. — 332 p. — ISBN: 978-0-12-815480-9. This book demonstrates that existing disruptive implications and applications of AI is a development of the unique attributes of neural networks, mainly machine learning, distributed architectures, massive parallel processing, black-box inference, intrinsic nonlinearity and smart autonomous search engines. The book...
Springer, 2013. — 204 p. This book introduces some applications of Computational Intelligence (CI) to problems of Earth System Science (ESS). In my opinion, the meeting of CI and ESSs is not a coincidence. There is an affinity between these two fields of science at a very deep level. Both of them use a systems approach; they see their object as a complex system of partly...
Springer, 2013. — 189 p. The primary purpose of this book is to show that a multilayer neural network can be considered as a multistage system, and then that the learning of this class of neural networks can be treated as a special sort of the optimal control problem. In this way, the optimal control problem methodology, like dynamic programming, with modifications, can yield a...
The University of Amsterdam, 1996. - 135 p. This manuscript attempts to provide the reader with an insight in artificial neural networks. Back in 1990, the absence of any state-of-the-art textbook forced us into writing our own. However, in the meantime a number of worthwhile textbooks have been published which can be used for background and in-depth information. We are aware...
IGI Global, 2023. — 267 p. Graph Neural Networks, also known as GNNs, have seen a meteoric rise in popularity over the past few years due to their capacity to analyze data that is shown in the form of graphs. GNNs have been put to use in a broad variety of industries, including social network research, the search for new drugs, recommender systems, and traffic prediction, to...
IGI Global, 2023. — 267 p. Graph Neural Networks, also known as GNNs, have seen a meteoric rise in popularity over the past few years due to its capacity to analyze data that is shown in the form of graphs. GNNs have been put to use in a broad variety of industries, including social network research, the search for new drugs, recommender systems, and traffic prediction, to...
Springer, 2010. — 225 p. Micromechanical manufacturing based on microequipment creates new possibilities in goods production. If microequipment sizes are comparable to the sizes of the microdevices to be produced, it is possible to decrease the cost of production drastically. The main components of the production cost - material, energy, space consumption, equipment, and...
Peter Lang, 2022. — 85 p. This book describes the functions frequently used in deep neural networks. Machine Learning. Types of Machine Learning. Supervised Learning. Regression. Classification and Logistic Regression. Unsupervised Learning. Clustering. Semi-supervised Learning. Reinforcement Learning. Federated Learning. Transfer Learning. Ensemble Learning. Neural Networks....
De Gruyter, 2014. — 173 p. The information processing in the brain is realized via spatiotemporal dynamics of patterns of excitation in neuronal ensembles arising by means of neuron interactions. Learning processes, equipped with the brain memory and self-organizing ability, modify the pattern structure and dynamics. A combination of analytical mathematical tools (such as...
De Gruyter, 2014. — 172 p. — ISBN: 978-3-11-026835-5, e-ISBN: 978-3-11-026920-8. Understanding of the human brain functioning currently represents a challenging problem. In contrast to usual serial computers and complicated hierarchically organized artificial man-made systems, decentralized, parallel and distributed information processing principles are inherent to the brain....
Diplomica, 2008. — 241 p. This Study about burnout in nurses takes a different approach than all prior empirical work on this topic given that nonlinear relationships between job stressors, personal factors and the three burnout dimensions are investigated using artificial neural networks, a type of computer simulation that is especially well suited to capturing nonlinearities...
Introduction to Neural Networks, Fuzzy Systems, Genetic Algorithms, and their Fusion A New Fuzzy-Neural Controller Expert Knowledge-Based Direct Frequency Converter Using Fuzzy Logic Control Design of an Electro-Hydraulic System Using Neuro-Fuzzy Techniques Neural Fuzzy Based Intelligent Systems and Applications Vehicle Routing through Simulation of Natural Processes Fuzzy...
Society for Industrial and Applied Mathematics, 2004, -107 p. When I first heard about neural networks and how great they were, I was rather skeptical. Being sold as a magical black box, there was enough hype to make one believe that they could solve the world's problems. When I tried to learn more about them, I found that most of the literature was written for a machine...
Springer, 1991. — 224 p. Current artificial neural network (ANN) models allow the networks to adjust their behavior by changing the interconnection weights associating neurons to each other, but the number of neurons and the structural relationship between neurons must be set up by system designers and once the structure is designed, it is fixed throughout the life cycle of the...
We investigate under what conditions a neuron can learn by experimentally supported rules for spike timing dependent plasticity (STDP) to predict the arrival times of strong teacher inputs to the same neuron. It turns out that in contrast to the famous Perceptron Convergence Theorem, which predicts convergence of the perceptron learning rule for a strongly simplified neuron...
Reward-modulated spike-timing-dependent plasticity (STDP) has recently emerged as a candidate for a learning rule that could explain how local learning rules at single synapses support behaviorally relevant adaptive changes in complex networks of spiking neurons. However the potential and limitations of this learning rule could so far only be tested through computer...
Springer, 2000. — 274 p. In this book, an easily understandable account of modeling methods with artificial neuronal networks for practical applications in ecology and evolution is provided. Special features include examples of applications using both supervised and unsupervised training, comparative analysis of artificial neural networks and conventional statistical methods,...
This volume is the first diverse and comprehensive treatment of algorithms and architectures for the realization of neural network systems. It presents techniques and diverse methods in numerous areas of this broad subject. The book covers major neural network systems structures for achieving effective systems, and illustrates them with examples. This volume includes Radial...
Academic Press, 1998, -485 p. Inspired by the structure of the human brain, artificial neural networks have been widely applied to fields such as pattern recognition, optimization, coding, control, etc., because of their ability to solve cumbersome or intractable problems by learning directly from data. An artificial neural network usually consists of a large number of simple...
Academic Press, 1998, -423 p. Inspired by the structure of the human brain, artificial neural networks have been widely applied to fields such as pattern recognition, optimization, coding, control, etc., because of their ability to solve cumbersome or intractable problems by learning directly from data. An artificial neural network usually consists of a large number of simple...
Academic Press, 1998, -421 p. Inspired by the structure of the human brain, artificial neural networks have been widely applied to fields such as pattern recognition, optimization, coding, control, etc., because of their ability to solve cumbersome or intractable problems by learning directly from data. An artificial neural network usually consists of a large number of simple...
Academic Press, 1998, -407 p. Inspired by the structure of the human brain, artificial neural networks have been widely applied to fields such as pattern recognition, optimization, coding, control, etc., because of their ability to solve cumbersome or intractable problems by learning directly from data. An artificial neural network usually consists of a large number of simple...
Academic Press, 1998, -437 p. Inspired by the structure of the human brain, artificial neural networks have been widely applied to fields such as pattern recognition, optimization, coding, control, etc., because of their ability to solve cumbersome or intractable problems by learning directly from data. An artificial neural network usually consists of a large number of simple...
Academic Press, 1998, -459 p. Inspired by the structure of the human brain, artificial neural networks have been widely applied to fields such as pattern recognition, optimization, coding, control, etc., because of their ability to solve cumbersome or intractable problems by learning directly from data. An artificial neural network usually consists of a large number of simple...
Springer, 2020. — 315 p. — (Advances in Computer Vision and Pattern Recognition). — ISBN: 978-3-030-42127-4. This book addresses one of the most important unsolved problems in artificial intelligence: the task of learning, in an unsupervised manner, from massive quantities of spatiotemporal visual data that are available at low cost. The book covers important scientific...
New York: Psychology Press, 2018. — 481 p. Providing a thorough introduction to the field of neural networks, this edition concentrates on networks for modeling brain processes involved in cognitive and behavioral functions. Part I explores the philosophy of modeling and the field’s history, starting from the mid-1940s, and then discusses past models of associative learning and...
New York: Dr. N.D Lewis, 2017. — 227 p. If you are anything like me, you hate long prefaces. I don’t care about the author’s background. Nor do I need a engthy overview of the history of what I am about to learn. Just tell me what I am going to learn, and then teach me how to do it. You are about to learn, through a series of projects, how to use a set of modern neural network...
Springer, 2018. — 86 p. In the past decades, recurrent neural networks have been widely investigated by many scientific and engineering communities. In particular, Hopfield neural network, originally designed for real-time optimization, triggers the studies on recurrent neural networks as a powerful online optimization tool. Recurrent neural network-based optimization methods...
New York: Springer, 2017. — 127 p. The first book to solve competition-based problems by means of various centralized or distributed neural network models Includes theoretical analyses, computer simulations, and robotic applications in neurocomputing fields Paves the way for the competition-based cooperative control of multiple redundant manipulators with limited communications...
Academic Press, 2019. — 183 p. — (Emerging Methodologies and Applications in Modeling). — ISBN: 978-0-12-815372-7. This book introduces nonlinear systems basic knowledge, analysis and control methods, and applications in various fields. It offers instructive examples and simulations, along with the source codes, and provides the basic architecture of control science and...
2nd Edition. — River Publishers, 2017. — 554 p. — (Series in Multi Business Model Innovation, Technologies and Sustainable Business). — ISBN: 978-87-93519-27-5. In the first decade of the 21st century product development in networks was predicted to be of ever-increasing importance to businesses of all sizes because of changes in markets, in technology, in networks, and in the...
Springer, 2001. — 224 p. It is well known that linear models have been widely used in system identification for two major reasons. First, the effects that different and combined input signals have on the output are easily determined. Second, linear systems are homogeneous. However, control systems encountered in practice possess the property of linearity only over a certain...
CRC Press, 2021. — 237 p. — ISBN: 978-1-032-03810-0. In this book, the stability analysis and estimator design problems are discussed for delayed discrete-time memristive neural networks. In each chapter, the analysis problems are firstly considered, where the stability, synchronization and other performances (e.g., robustness, disturbances attenuation level) are investigated...
Springer, 2013. — 373 p. Radial Basis Function (RBF) Neural Network Control for Mechanical Systems is motivated by the need for systematic design approaches to stable adaptive control system design using neural network approximation-based techniques. The main objectives of the book are to introduce the concrete design methods and MatLAB simulation of stable adaptive RBF neural...
World Scientific, 2004, -395 p. This book systematically synthesizes research achievements in the field of fuzzy neural networks in recent years. It also provides a comprehensive presentation of the developments in fuzzy neural networks, with regard to theory as well as their application to system modeling and image restoration. Special emphasis is placed on the fundamental...
Humana Press, 2008. — 332 p. Artificial neural networks (ANN) is the name given to a branch of artificial intelligence (AI) research that aims to simulate intelligent behavior by mimicking the way that biological neural networks work. Most AI methods seek to reproduce human intelligence by imitating “what we do,” ANN seek to reproduce it by imitating “the way that we do it.”...
2nd Edition. — Apress Media LLC, 2022. — 640 p. — ISBN13: 978-1-4842-7367-8. Develop neural network applications using the Java environment. After learning the rules involved in neural network processing, this second edition shows you how to manually process your first neural network example. The book covers the internals of front and backpropagation and helps you understand...
Apress, 2019. — 574 p. Use Java to develop neural network applications in this practical book. After learning the rules involved in neural network processing, you will manually process the first neural network example. This covers the internals of front and back propagation, and facilitates the understanding of the main principles of neural network processing. Artificial Neural...
Apress, 2019. — 640 p. — ISBN-13 (electronic): 978-1-4842-4421-0. Use Java to develop neural network applications in this practical book. After learning the rules involved in neural network processing, you will manually process the first neural network example. This covers the internals of front and back propagation, and facilitates the understanding of the main principles of...
Apress, 2019. — 640 p. — ISBN-13 (electronic): 978-1-4842-4421-0. Use Java to develop neural network applications in this practical book. After learning the rules involved in neural network processing, you will manually process the first neural network example. This covers the internals of front and back propagation, and facilitates the understanding of the main principles of...
Apress, 2019. — 640 p. — ISBN-13 (electronic): 978-1-4842-4421-0. Use Java to develop neural network applications in this practical book. After learning the rules involved in neural network processing, you will manually process the first neural network example. This covers the internals of front and back propagation, and facilitates the understanding of the main principles of...
Apress, 2019. — 575 p. — ISBN13: 978-1-4842-4420-3. Use Java to develop neural network applications in this practical book. After learning the rules involved in neural network processing, you will manually process the first neural network example. This covers the internals of front and back propagation, and facilitates the understanding of the main principles of neural network...
2nd Edition. — Apress Media LLC, 2022. — 640 p. — ISBN-13 (electronic): 978-1-4842-7368-5. Develop neural network applications using the Java environment. After learning the rules involved in neural network processing, this second edition shows you how to manually process your first neural network example. The book covers the internals of front and backpropagation and helps you...
Packt Publishing Ltd., 2019. — 301 p. — ISBN: 978-1-78913-890-0. Build your Machine Learning portfolio by creating 6 cutting-edge Artificial Intelligence projects using neural networks in Python Neural networks are at the core of recent AI advances, providing some of the best resolutions to many real-world problems, including image recognition, medical diagnosis, text analysis,...
Packt Publishing Ltd., 2019. — 301 p. — ISBN: 978-1-78913-890-0. Build your Machine Learning portfolio by creating 6 cutting-edge Artificial Intelligence projects using neural networks in Python Neural networks are at the core of recent AI advances, providing some of the best resolutions to many real-world problems, including image recognition, medical diagnosis, text analysis,...
Packt Publishing Ltd., 2019. — 385 p. — ISBN: 978-1-78913-890-0. Build your Machine Learning portfolio by creating 6 cutting-edge Artificial Intelligence projects using neural networks in Python Neural networks are at the core of recent AI advances, providing some of the best resolutions to many real-world problems, including image recognition, medical diagnosis, text analysis,...
Packt Publishing Ltd., 2019. — 385 p. — ISBN: 978-1-78913-890-0. Build your Machine Learning portfolio by creating 6 cutting-edge Artificial Intelligence projects using neural networks in Python Neural networks are at the core of recent AI advances, providing some of the best resolutions to many real-world problems, including image recognition, medical diagnosis, text analysis,...
Packt Publishing Ltd., 2019. — 385 p. — ISBN: 978-1-78913-890-0. Build your Machine Learning portfolio by creating 6 cutting-edge Artificial Intelligence projects using neural networks in Python Neural networks are at the core of recent AI advances, providing some of the best resolutions to many real-world problems, including image recognition, medical diagnosis, text analysis,...
Leanpub, Eric Ma and Mridul Seth, 2021. – 191 p. Are you interested in learning about graph theory and applied network analysis, leveraging your Python skills? Then this is the book for you! See how network science & graph theory connects with a variety of data analysis problems, and use it to solve your next Data Science challenge! As the accompanying book to the popular...
Springer, 2010. — 212 p. There is no more complicated, advantaged and powerful device than the mammalian primate cortical visual system for image processing in nature. The pulse-coupled neural network (PCNN) is inspired from the investigation of pulse synchronization within the mammalian visual cortex, and has been widely applied to image processing and pattern recognition....
Institute for Theoretical Computer Science Graz University of Technology The Liquid State Machine (LSM) has emerged as a computational model that is more adequate than the Turing machine for describing computations in biological networks of neurons. Characteristic features of this new model are (i) that it is a model for adaptive computational systems, (ii) that it provides a...
The computational power of formal models for networks of spiking neurons is compared with that of other neural network models based on McCulloch Pitts neurons (i.e. , threshold gates), respectively, sigmoidal gates. In particular it is shown that networks of spiking neurons are, with regard to the number of neurons that are needed, computationally more powerful than these other...
Academic Press, 1999. Spiking neurons are models for the computational units in biological neural systems where information is considered to be encoded mainly in the temporal patterns of their activity. In a network of spiking neurons a new set of parameters becomes relevant which has no counterpart in traditional neural network models: the time that a pulse needs to travel...
Robert Gordon University, Aberdeen, Scotland, 2001, -157 p. An introductory course on Neural Networks and Genetic Algorithms. Part A – Neural Networks An introduction to Neural Networks Artificial Neural Networks The Back Propagation Algorithm Some illustrative applications feed forward networks Pre-processing input data Network layers and size Hopfield and recurrent networks...
Dissertation, Robert Gordon University, Aberdeen, Scotland, 1999, -275 p. The research presented in this thesis is concerned with optimising the structure of Artificial Neural Networks. These techniques are based on computer modeling of biological evolution or foetal development. They are known as Evolutionary, Genetic or Embryological methods. Specifically, Embryological...
Inductive Learning Algorithms for Complex Systems Modeling is a professional monograph that surveys new types of learning algorithms for modeling complex scientific systems in science and engineering. The book features discussions of algorithm development, structure, and behavior; comprehensive coverage of all types of algorithms useful for this subject; and applications of...
World Scientific Publishing Company, 2024. — 436 p. — (Nonlinear Science, Series A Vol. 99). — ISBN: 978-981-12-9008-4. This book argues for neuromorphic systems as a technology of the future, which are oriented towards the energy efficiency of natural brains. Energy efficiency is a dramatic claim in times of environmental and climate challenges which should consider the...
New York: Springer, 2013. — 142 p. Computational Neuroscience - A First Course provides an essential introduction to computational neuroscience and equips readers with a fundamental understanding of modeling the nervous system at the membrane, cellular, and network level. The book, which grew out of a lecture series held regularly for more than ten years to graduate students in...
John Wiley, 2001, -295 p. New technologies in engineering, physics and biomedicine are creating problems in which nonstationarity, nonlincarity, uncertainty and complexity play a major rote. Solutions to many of these problems require the use of nonlinear processors, among which neural networks are one of the most powerful. Neural networks are appealing because they learn, by...
London: Springer, 1999. — 390 p. From its early beginnings in the fifties and sixties, the field of neural networks has been steadily developing to become one of the most interdisciplinary areas of research within computer science. This volume contains selected papers from WIRN Vietri-98, the 10th Italian Workshop on Neural Nets, 21-23 May 1998, Vietri sul Mare, Salerno, Italy....
Springer, 2001. — 264 p. The expression 'Neural Networks' refers traditionally to a class of mathematical algorithms that obtain their proper performance while they 'learn' from examples or from experience. As a consequence, they are suitable performing straightforward and relatively simple tasks like classification, pattern recognition and prediction, as well as more...
Morgan Kaufmann, 1993. — 493 p. This text serves as a cookbook for neural network solutions to practical problems using C++. It will enable those with moderate programming experience to select a neural network model appropriate to solving a particular problem and to produce a working program implementing that network. The book guides along the entire problem-solving path,...
English 1993 Academic Press 504 p. This text serves as a cookbook for neural network solutions to practical problems using C++. It will enable those with moderate programming experience to select a neural network model appropriate to solving a particular problem, and to produce a working program implementing that network. The book provides guidance along the entire...
Academic Press, 1993. This text serves as a cookbook for neural network solutions to practical problems using C++. It will enable those with moderate programming experience to select a neural network model appropriate to solving a particular problem and to produce a working program implementing that network. The book guides along the entire problem-solving path, including...
Syncfusion Inc., 2014. — 128 p. With Neural Networks Using C# Succinctly by James McCaffrey, you’ll learn how to create your own neural network to solve classification problems, or problems where the outcomes can only be one of several values. Learn how to encode and normalize qualitative data into numeric data a neural network can use, different activation functions and when...
Morrisville (NC): Syncfusion, 2019. — 162 p. This e-book assumes you have intermediate or better programming skills, but does not assume you know anything about neural networks. This e-book presents complete example programs for the three major types of neural network problems. A multiclass classification problem predicts a discrete value where there are three or more possible...
Apress, 2018. — 139 p. — ISBN-13 9781484235065. Learn how to implement and build a neural network with this non-technical, project-based book as your guide. As you work through the chapters, you'll build an electronics project, providing a hands-on experience in training a network. There are no prerequisites here and you won't see a single line of computer code in this book....
Apress, 2018. — 139 p. — ISBN-13 9781484235065. Learn how to implement and build a neural network with this non-technical, project-based book as your guide. As you work through the chapters, you'll build an electronics project, providing a hands-on experience in training a network. There are no prerequisites here and you won't see a single line of computer code in this book....
Springer, 1994. — 241. Three years ago, when I started presenting tutorials on the integration neural networks and expert systems, I could uncover only enough work in area to fill one page of references. Today we see rapidly growing interest and order of magnitude more projects on hybrid systems that combine neural networks and expert systems. Working systems have been...
CRC Press, 2000. — 391 p. Recurrent neural networks have been an interesting and important part of neural network research during the 1990's. They have already been applied to a wide variety of problems involving time sequences of events and ordered data such as characters in words. Novel current uses range from motion detection and music synthesis to financial forecasting....
Cambridge University Press, 2022. — 261 p. This modern and self-contained book offers a clear and accessible introduction to the important topic of machine learning with neural networks. In addition to describing the mathematical principles of the topic, and its historical evolution, strong connections are drawn with underlying methods from statistical physics and current...
A Bradford Book, 1997. — 345 p. This most readable book gives a clear, up-to-date and concise introduction to artificial neural networks. It covers all the major network models and provides insightful information on their applications. I thoroughly recommend it to senior undergraduates, first-year graduate students and practicing engineers seeking an accessible lead-in to this...
Springer, 2012, -216 p. We describe in this book, hybrid intelligent systems using type-2 fuzzy logic and modular neural networks for pattern recognition applications. Hybrid intelligent systems combine several intelligent computing paradigms, including fuzzy logic, neural networks, and bio-inspired optimization algorithms, which can be used to produce powerful pattern...
Springer, 2024. — 204 p. — (Studies in Computational Intelligence 1146). — ISBN: 978-3-031-53712-7. We describe in this book new directions on the theoretical developments of fuzzy logic, neural networks, and meta-heuristic algorithms, as well as their hybrid combinations, and their application in areas such as intelligent control and robotics, pattern recognition, medical...
Springer, 2024. — 204 p. — (Studies in Computational Intelligence 1146). — ISBN: 978-3-031-53712-7. We describe in this book new directions on the theoretical developments of fuzzy logic, neural networks, and meta-heuristic algorithms, as well as their hybrid combinations, and their application in areas such as intelligent control and robotics, pattern recognition, medical...
Springer-Verlag Berlin Heidelberg. 2005. - 280 p. The monograph addresses the following issues: Introduction to Pattern Recognition with Intelligent Systems Type-1 Fuzzy Logic Intuitionistic and Type-2 Fuzzy Logic Supervised Learning Neural Networks Unsupervised Learning Neural Networks Modular Neural Networks Evolutionary Computing for Architecture Optimization Clustering with...
Springer, 2022. — 211 p. — eBook ISBN: 978-3-658-40004-0. Artificial intelligence (AI) is a concept, whose meaning and perception have changed considerably over the last decades. Starting off with individual and purely theoretical research efforts in the 1950s, AI has grown into a fully developed research field of modern times and may arguably emerge as one of the most...
Marcel Dekker, 2002. — 504 p. In the present book we concern ourselves exclusively with the qualitative analysis and synthesis of recurrent artificial neural networks. There is an abundance of papers, monographs, and books that address qualitative properties of such networks. However, whereas in these works, the analysis of the networks is frequently incidental to the...
Apress, 2018. — 425 p. — ISBN: 1484237897. Code files only! Work with advanced topics in deep learning, such as optimization algorithms, hyper-parameter tuning, dropout, and error analysis as well as strategies to address typical problems encountered when training deep neural networks. You’ll begin by studying the activation functions mostly with a single neuron (ReLu, sigmoid,...
Hoboken: Wiley, 2023. — 243 p. Systems Engineering Neural Networks a complete and authoritative discussion of systems engineering and neural networks. In Systems Engineering Neural Networks, a team of distinguished researchers delivers a thorough exploration of the fundamental concepts underpinning the creation and improvement of neural networks with a systems engineering...
Wiley, 2023. — – 243 p. — ISBN: 9781119901990. Systems Engineering Neural Networks a complete and authoritative discussion of systems engineering and neural networks. In Systems Engineering Neural Networks, a team of distinguished researchers delivers a thorough exploration of the fundamental concepts underpinning the creation and improvement of neural networks with a systems...
MIT Press, 1987. — 311 p. — ISBN: 0262631113, 9780262631112 Perceptrons — the first systematic study of parallelism in computation — has remained a classical work on threshold automata networks for nearly two decades. It marked a historical turn in artificial intelligence, and it is required reading for anyone who wants to understand the connectionist counterrevolution that is...
The MIT Press, 1968. — 452 p. — ISBN: 9780262130448, 0262130440. This book collects a group of experiments directed toward making intelligent machines. Each of the programs described here demonstrates some aspect of behavior that anyone would agree requires some intelligence, and each program solves its kinds of problems. These include resolving ambiguities in word meanings,...
Delft: Now Publishers, 2018. — 129 p. Fundamentals of text retrieval. IR tasks. Desiderata of IR models. Metrics. Traditional IR models. Neural approaches to IR. Unsupervised learning of term representations. A tale of two representations. Notions of similarity. Observed feature spaces. Latent feature spaces. Term embeddings for IR. Query-document matching. Query expansion....
Springer, 2014. — 261 p. — ISBN: 9783319110714, 9783319110707. The papers demonstrate a variety of applications of neural networks and other computational intelligence approaches to challenging problems relevant to society and the economy. These include areas such as: environmental engineering, facial expression recognition, classification with parallelization algorithms,...
Springer, 2020. — 241 p. — ISBN: 978-3-030-37223-1. Introduces in one volume all the trends that can be used to overcome Moore’s law limitations. Describes in detail Neuromorphic, Approximate, Parallel, In Memory, and Quantum Computing concepts, in a manner accessible to a wide variety of readers. Compares tradeoffs between the various paradigms discussed. An Introduction: New...
Springer, 2022. — 123 p. — ISBN: 978-3-031-14570-4. This book explains the basic concepts, theories, and applications of neural networks in a simple unified approach with clear examples and simulations in the MatLAB programming language. The scripts herein are coded for general purposes to be easily extended to a variety of problems in different areas of application. They are...
Packt Publishing, 2018. — 218 p. — ISBN: 978-1-78839-230-3. Convolutional Neural Network (CNN) is revolutionizing several application domains such as visual recognition systems, self-driving cars, medical discoveries, innovative eCommerce and more.You will learn to create innovative solutions around image and video analytics to solve complex machine learning and computer vision...
Packt Publishing, 2018. — 218 p. — ISBN: 978-1-78839-230-3. Convolutional Neural Network (CNN) is revolutionizing several application domains such as visual recognition systems, self-driving cars, medical discoveries, innovative eCommerce and more.You will learn to create innovative solutions around image and video analytics to solve complex machine learning and computer vision...
Springer, 2012, -753 p. There have been substantial changes in the field of neural networks since the first edition of this book in 1998. Some of them have been driven by external factors such as the increase of available data and computing power. The Internet made public massive amounts of labeled and unlabeled data. The ever-increasing raw mass of user-generated and sensed...
New York: Springer, 2018. — 99 p. Acronyms Deep Learning Background Incorporating Structural Information into Neural Architectures Proposed Tree-based Convolutional NNs Structure of the Book Refs Neuron & Multilayer Network Training Objectives Learning Neural Parameters Pretraining Neural Networks Neural Networks for NLP Neural Language Models Word Embeddings Convolutional...
Springer, 2014, -196 p. The quality of models of systems and processes determines the effectiveness of numerous contemporary technical systems i.e., control systems, fault diagnosis systems or fault tolerant control systems. Over the last few decades the scientists and engineers intensively look for the efficient modeling methods of the systems and processes and this book joins...
Imperial College, 2001, -418 p. This book is a follow-up of the IChemE CAPESG workshop on "The Application of Neural Networks and Other Learning Technologies in Process Engineering" held on the 12th May 1999 at Imperial College, London. The interest showed by the participants especially those from the industries in these emerging technologies has inspired us to come up with...
Wiley-IEEE Press, 2023. — 304 p. — ISBN: 978-1-394-17188-0. Accelerators for Convolutional Neural Networks. Comprehensive resource exploring different types of convolutional neural networks and complementary accelerators. Accelerators for Convolutional Neural Networks provides basic deep learning knowledge and instructive content to build up convolutional neural network (CNN)...
Springer, 2021. — 239 p. — ISBN: 978-981-33-4975-9. From theory to application, this book presents research on biologically and brain-inspired networking and machine learning based on Yuragi, which is the Japanese term describing the noise or fluctuations that are inherently used to control the dynamics of a system. The Yuragi mechanism can be found in various biological...
New Age, 2005, -168 p. This book deals with a novel paradigm of neural networks, called multidimensional neural networks. It also provides comprehensive description of a certain unified theory of control, communication and computation. This book can serve as a textbook for an advanced course on neural networks or computational intelligence/cybernetics. Both senior undergraduate...
Springer, 2001. — 420 p. Topic: Neural networks in MatLAB, MLP, RBF, GMM, Monte Carlo, EM. Contains a guide to the NETLAB neural network modeling framework (MLP, RBF, etc.) and ML techniques developed under MatLAB at Ashton University, UK. Getting the most out of neural networks and related data modeling techniques is the purpose of this book. The text, with the accompanying...
New York: Springer, 2021. — 463 p. This book is the first comprehensive book about reservoir computing (RC). RC is a powerful and broadly applicable computational framework based on recurrent neural networks. Its advantages lie in small training data set requirements, fast training, inherent memory and high flexibility for various hardware implementations. It originated from...
N.Y.: Springer Science+Business Media, 1996. — 183 p. — (Series Lecture Notes in Statistics, Vol. 118). — ISBN: 978-0-387-94724-2. This book explores the Bayesian approach to learning flexible statistical models based on what are known as "neural networks". These models are now commonly used for many applications, but understanding why they (sometimes) work well and how they...
Boca Raton: CRC Press, 1999. — 417 p. Information theoretics vis-a-vis neural networks generally embodies parametric entities and conceptual bases pertinent to memory considerations and information storage, information-theoretic based cost-functions, and neurocybernetics and self-organization. Existing studies only sparsely cover the entropy and/or cybernetic aspects of neural...
Nova Publishers, 2011. — 341 p. This book gathers the most current research from across the globe in the study of artificial neural networks. Topics discussed include a neural network based visual servo system; modeling of computer-assisted learning using artificial neural networks; prediction of hole quality in drilling GFRE using artificial neural networks; ANN-based...
IGI Global, 2009. — 504 p. Recent research indicates that complex-valued neural networks whose parameters (weights and threshold values) are all complex numbers are in fact useful, containing characteristics bringing about many significant applications. Complex-Valued Neural Networks: Utilizing High-Dimensional Parameters covers the current state-of-the-art theories and...
Information SCience Reference, 2009. — 504 p. Recent research indicates that complex-valued neural networks whose parameters (weights and threshold values) are all complex numbers are in fact useful, containing characteristics bringing about many significant applications. Complex-Valued Neural Networks: Utilizing High-Dimensional Parameters covers the current state-of-the-art...
Academic Press, 1997. — 375 p. Control problems offer an industrially important application and a guide to understanding control systems for those working in Neural Networks. Neural Systems for Control represents the most up-to-date developments in the rapidly growing aplication area of neural networks and focuses on research in natural and artifical neural systems directly...
CRC Press, 2022 — 159 p. — ISBN: 0367211467 The world is on the verge of fully ushering in the fourth industrial revolution, of which artificial intelligence (AI) is the most important new general-purpose technology. Like the steam engine that led to the widespread commercial use of driving machinery in the industries during the first industrial revolution; the internal...
ITexLi, 2024. — 289 p. — ISBN: 0850141184 9780850141184 0850141176 9780850141177 0850141192 9780850141191. This volume is dedicated to a brand-new perspective on how education will help us build the future. This book provides fresh insights into top-of-the-line strategies and developing trends while delving deep into a variety of future perspectives on lifelong learning and the...
MIT Press, 1994, -289 p. Neural networks usually work adequately on small problems but can run into trouble when they are scaled up to problems involving large amounts of input data. Circuit Complexity and Neural Networks addresses the important question of how well neural networks scale - that is, how fast the computation time and number of neurons grow as the problem size...
Mit Press, 1998. Neural networks began to be the object of serious research in the 1940s. They did not become widely known or popular until the 1980s when several academic centers published articles and distributed software that enabled individuals with modest mathematical and computing skills to learn computational network principles and apply them to broad ranges of projects...
Springer, 2008. — 223 p. It is well understood that fault diagnosis has become an important issue in modern automatic control theory. Early diagnosis of faults that might occur in the supervised process renders it possible to perform important preventing actions. Moreover, it allows one to avoid heavy economic losses involved in stopped production, the replacement of elements...
New York: Springer, 2018. — 108 p. This book guides readers along a path that proceeds from neurobiology to nonlinear-dynamical circuits, to nonlinear neuro-controllers and to bio-inspired robots. It provides a concise exploration of the essence of neural processing in simple animal brains and its adaptation and extrapolation to modeling, implementation, and realization of the...
IGI Global , 2020. — 315 p. — ISBN: 1799840425. Processing information and analyzing data efficiently and effectively is crucial for any company that wishes to stay competitive in its respective market. Nonlinear data presents new challenges to organizations, however, due to its complexity and unpredictability. The only technology that can properly handle this form of data is...
Springer, 2008 — 124 p. This book presents the Neuroprocessor, a novel computational neuronal interface device implemented in VLSI technology. In addition to neuronal signals acquisition, it can process the data, generate stimuli and transmit the data over wireless channels, while using minimum electric energy. The NeuroProcessor opens with a brief background on neuronal...
Cambridge University Press 1992, 492 c. Mind as an emergent property of nervous systems Neuronal nets as automata networks: a brief historical overview Organization of the book The biology of neural networks: a few features for the sake of non-biologists Three approaches to the study of the functioning of central nervous systems The anatomy of central nervous systems A brief...
Oxford University Press, 2001. — 497 p. Neural Networks and Intellect: Using Model-Based Concepts describes a new mathematical concept of modeling field theory and its applications to a variety of problems. Examining the relationships among mathematics, computations in neural networks, signs and symbols in semiotics, and ideas of mind in psychology and philosophy, this unique...
Springer, London, 1995. — 242 p. — ISBN13: 978-1-4471-3246-2. In recent years, there has been a growing interest in applying neural networks to dynamic systems identification (modeling), prediction and control. Neural networks are computing systems characterised by the ability to learn from examples rather than having to be programmed in a conventional sense. Their use enables...
Pergamon, 2001. — 351 p. This book was primarily written for an audience that has heard about neural networks or has had some experience with the algorithms, but would like to gain a deeper understanding of the fundamental material. For those that already have a solid grasp of how to create a neural network application, this work can provide a wide range of examples of nuances...
World Scientific, 2001, -454 p. This book deals with Continuous Time Dynamic Neural Networks Theory applied to solution of basic problems arising in Robust Control Theory including identification, state space estimation (based on neuro observers) and trajectory tracking. The plants to be identified and controlled are assumed to be a priory unknown but belonging to a given class...
Springer, 2008, -430 p. Neural networks play an important role in several scientific and industrial applications. This peer reviewed and edited book presents some recent advances on the application of neural networks in the areas of speech, audio, image and biomedical signal processing. Each chapter in the book is reviewed by at least two independent experts in the topical...
SPIE Press, 2005. — 181 p. This text introduces the reader to the fascinating world of artificial neural networks, a journey that the authors are here to help you with. The authors have written this book for the reader who wants to understand artificial neural networks without necessarily being bogged down in the mathematics. A glossary is included to assist the reader in...
IEEE/John Wiley, 2007. — 119 p. At NASA, adaptive systems have left the experimental stage and are now becoming practical solution to the demands of our technological goals for many of our projects, including those with high criticality and dependability. Neural networks are a logical development and operational solution to adaptive system applications and, as such, NASA needs...
London, UK: Academic Press ; Elsevier, 2016. — 440 p. — ISBN: 978-0-12-801744-9. Artificial Neural Network for Drug Design, Delivery and Disposition provides an in-depth look at the use of artificial neural networks (ANN) in pharmaceutical research. With its ability to learn and self-correct in a highly complex environment, this predictive tool has tremendous potential to help...
Packt Publishing, 2019. — 462 p. — ISBN13: 978-1-78953-608-9. Your one-stop guide to learning and implementing artificial neural networks with Keras effectively Key Features Design and create neural network architectures on different domains using Keras Integrate neural network models in your applications using this highly practical guide Get ready for the future of neural...
Psychology Press, 2003. — 373 p. Connectionist Models of Development is an edited collection of essays on the current work concerning connectionist or neural network models of human development. The brain comprises millions of nerve cells that share myriad connections, and this book looks at how human development in these systems is typically characterised as adaptive changes...
CRC Press, 2013. — 623 p. Understanding how populations of neurons encode information is the challenge faced by researchers in the field of neural coding. Focusing on the many mysteries and marvels of the mind has prompted a prominent team of experts in the field to put their heads together and fire up a book on the subject. Simply titled Principles of Neural Coding, this book...
Idea Group Inc., 2006. — 394 p. — ISBN: 1591409020, 9781591409021 Throughout the past, human beings have been concerned with how to acquire tools that might increase their potentialities, not only regarding the physical or intellectual aspect but also the metaphysical one. All these studies and investigations have achieved spectacular results, although they are still far from...
New Delhi: PHI Learning Private Limited, 2021. — This book provides comprehensive introduction to a consortium of technologies underlying soft computing. The constituent technologies discussed comprise neural networks, fuzzy logic, genetic algorithms, and a number of hybrid systems which include classes such as neuro-fuzzy, fuzzy-genetic, and neuro-genetic systems. The...
Prentice-Hall, 2017. — 572 p. — ISBN13: 978-8120353343. This book provides comprehensive introduction to a consortium of technologies underlying soft computing, an evolving branch of computational intelligence. The constituent technologies discussed comprise neural networks, fuzzy logic, genetic algorithms, and a number of hybrid systems which include classes such as...
Prentice-Hall, 2017. — 572 p. — ISBN13: 978-8120353343. This book provides comprehensive introduction to a consortium of technologies underlying soft computing, an evolving branch of computational intelligence. The constituent technologies discussed comprise neural networks, fuzzy logic, genetic algorithms, and a number of hybrid systems which include classes such as...
Prentice-Hall, 2017. — 572 p. — ISBN13: 978-8120353343. This book provides comprehensive introduction to a consortium of technologies underlying soft computing, an evolving branch of computational intelligence. The constituent technologies discussed comprise neural networks, fuzzy logic, genetic algorithms, and a number of hybrid systems which include classes such as...
Springer, 2012. — 234 p. A significant amount of effort in neural modeling is directed towards understanding the representation of information in various parts of the brain, such as cortical maps, and the paths along which sensory information is processed. Though the time domain is integral an integral aspect of the functioning of biological systems, it has proven very...
CRC Press, 2020. - 248 p. - ISBN: 9781138364509 Statistical Learning using Neural Networks: A Guide for Statisticians and Data Scientists with Python introduces artificial neural networks starting from the basics and increasingly demanding more effort from readers, who can learn the theory and its applications in statistical methods with concrete Python code examples. It...
We investigated whether it is possible to infer spike trains solely on the basis of the underlying local field potentials (LFPs). Using support vector machines and linear regression models, we found that in the primary visual cortex (V1) of monkeys, spikes can indeed be inferred from LFPs, at least with moderate success. Although there is a considerable degree of variation...
CreateSpace, 2016. — 222 p. A gentle journey through the mathematics of neural networks, and making your own using the Python computer language. Neural networks are a key element of deep learning and artificial intelligence, which today is capable of some truly impressive feats. Yet too few really understand how neural networks actually work. This guide will take you on a fun...
CreateSpace, 2016. — 222 p. A step-by-step gentle journey through the mathematics of neural networks, and making your own using the Python computer language. Neural networks are a key element of deep learning and artificial intelligence, which today is capable of some truly impressive feats. Yet too few really understand how neural networks actually work. This guide will take...
CreateSpace, 2016. — 222 p. A step-by-step gentle journey through the mathematics of neural networks, and making your own using the Python computer language. Neural networks are a key element of deep learning and artificial intelligence, which today is capable of some truly impressive feats. Yet too few really understand how neural networks actually work. This guide will take...
CreateSpace, 2016. — 222 p. A step-by-step gentle journey through the mathematics of neural networks, and making your own using the Python computer language. Neural networks are a key element of deep learning and artificial intelligence, which today is capable of some truly impressive feats. Yet too few really understand how neural networks actually work. This guide will take...
New York: Academic Press, 2014. — 283 p. — ISBN: 978-0-12-410407-5 Calculus of Thought: Neuromorphic Logistic Regression in Cognitive Machines is a must-read for all scientists about a very simple computation method designed to simulate big-data neural processing. This book is inspired by the Calculus Ratiocinator idea of Gottfried Leibniz, which is that machine computation...
New York: Springer, 2017. — 93 p. This book shows how information systems (IS) scholars can effectively apply neuroscience expertise in ways that do not require neuroscience tools. However, the approach described here is intended to complement neuroscience tools, not to supplant them. Written by leading scholars in the field, it presents a review of the empirical literature on...
Springer, 2015, -296 p. This book provides a complete study on neural structures exhibiting nonlinear and stochastic dynamics. The book elaborates on neural dynamics by introducing advanced models of neural networks. It overviews the main findings in the modeling of neural dynamics in terms of electrical circuits and examines their stability properties with the use of dynamical...
Academic Press, 2020. — 151 p. — ISBN: 978-0-12-817078-6. This book focuses on modeling and control of discrete-time unknown nonlinear delayed systems under uncertainties based on Artificial Neural Networks. First, a Recurrent High Order Neural Network (RHONN) is used to identify discrete-time unknown nonlinear delayed systems under uncertainties, then a RHONN is used to design...
N. Y.: Addison-Wesley, 1992. — 293 p. This book is a comprehensive introduction to neural networks and neural information processing. It describes the most important models of neural networks and how they contribute to our understanding of information and organization processes in the brain. One of the few generally recognized organizational principles of the nervous system,...
Springer, 1996. — 512 p. This book arose from my lectures on neural networks at the Free University of Berlin and later at the University of Halle. I started writing a new text out of dissatisfaction with the literature available at the time. Most books on neural networks seemed to be chaotic collections of models and there was no clear unifying theoretical thread connecting...
New York: ITexLi, 2016. — 409 p. This is a current book on Artificial Neural Networks and Applications, bringing recent advances in the area to the reader interested in this always-evolving machine learning technique. The idea of simulating the brain was the goal of many pioneering works in Artificial Intelligence. The brain has been seen as a neural network, or a set of nodes,...
Chapman & Hall, 1996. — 311 p. Over the past several years a great deal of research has been carried out in the areas of artificial vision and neural networks. Although much of this research has been theoretical in nature, many of the techniques developed through these efforts are now mature enough to be used in practical applications. Concurrently, the opening of worldwide...
The MIT Press, 2024. — 168 p. — ISBN: 978-0262378758. An accessible undergraduate textbook in computational neuroscience that provides an introduction to the mathematical and computational modeling of neurons and networks of neurons. Understanding the brain is a major frontier of modern science. Given the complexity of neural circuits, advancing that understanding requires...
Rosenblatt F. Principles of neurodynamics: Perceptions and the theory of brain mechanism. Washington DC, Spartan Books, 1961. 616 p. Development of basic concepts Historical review of alternative approaches. Physiological and psychological considerations. Basic definitions and concepts. Three-layer series-coupled perceptrons The existence and attainability of solutions in...
Springer, 2000. — 203 p. Recent technological developments have forced control engineers to deal with extremely complex systems that include uncertain, and possibly unknown, nonlinearities, operating in highly uncertain environments. The above, together with continuously demanding performance requirements, place control engineering as one of the most challenging technological...
New York: Springer, 2009. - 311 p. Recent experimental research advances have led to increasingly detailed descriptions of how networks of interacting neurons process information. With these developments, it has become clear that dynamic network behaviors underlie information processing, and that the observed activity patterns cannot be fully explained by simple concepts such...
Springer, 2002. — 292 p. The advent of the computer age has set in motion a profound shift in our perception of science -its structure, its aims and its evolution. Traditionally, the principal domains of science were, and are, considered to be mathe matics, physics, chemistry, biology, astronomy and related disciplines. But today, and to an increasing extent, scientific...
CRC Press, 2023. — 220 p. — ISBN: 9781003303053. This textbook comprehensively discusses the latest mathematical modeling techniques and their applications in various areas such as fuzzy modeling, signal processing, neural network, machine learning, image processing, and numerical analysis. It further covers image processing techniques such as the Viola-Jones method for face...
De Gruyter, 2022. — 154 p. — ISBN: 978-3-11-065626-8. Neural Networks are an integral part of machine learning and a known tool for controlling nonlinear processes. The area is under rapid development and provides a tool for modeling and controlling of advanced processes. This book provides a comprehensive overview of modeling, simulation, measurement, and control strategies...
Springer, 2022. — 130 p. — ISBN13: 9783030899288. This textbook provides a compact but comprehensive treatment that provides analytical and design steps to recurrent neural networks from scratch. It provides a treatment of the general recurrent neural networks with principled methods for training that render the (generalized) backpropagation through time (BPTT). This author...
Auerbach Publications, 2006, -582 p. This book is an exploration of neural networks for pattern recognition in scientific data. An important highlight is the extensive visual presentation of neural networks concepts throughout. This book is motivated by the necessity for a text that caters to both researchers and students from a wide range of backgrounds, one that puts neural...
Academic Press, 2017. — 620 p. — ISBN: 978-0-12-811318-9. This Handbook explores neural computation applications, ranging from conventional fields of mechanical and civil engineering, to electronics, electrical engineering and computer science. This book covers the numerous applications of artificial and deep neural networks and their uses in learning machines, including image...
Springer, 2012. — 193 p. This book is essentially the first author’s Ph.D. thesis, which was successfully defended at the University of Leicester in 2009. It explores the feasibility of two technologies in reducing cost and weight of air vehicles. The first is a fault detection and isolation scheme, which uses neural networks to diagnose faults in sensors. The second is a flush...
Springer, 2017. — 105 p. In this book, a new model of modular neural network based on a granular approach, the combination of their responses, and the optimization by hierarchical genetic algorithms are introduced. The new model of modular neural networks is applied to human recognition, and for this four databases of biometric measures are used; face, iris, ear, and voice. The...
Springer, 2008. — 116 p. Neural networks have become a well-established methodology as exemplified by their applications to identification and control of general nonlinear and complex systems; the use of high order neural networks for modeling and learning has recently increased. Using neural networks, control algorithms can be developed to be robust to uncertainties and...
Springer, 2003, -336 p. The past fifteen years has witnessed an explosive growth in the fundamental research and applications of artificial neural networks (ANNs) and fuzzy logic (FL). The main impetus behind this growth has been the ability of such methods to offer solutions not amenable to conventional techniques, particularly in application domains involving pattern...
Apress, 2021. — 726 p. — ISBN: 1484261496. Develop machine learning models across various domains. This book offers a single source that provides comprehensive coverage of the capabilities of TensorFlow 2 through the use of realistic, scenario-based projects.
CRC Press, 2000, -367 p. Modern feedback control systems have been responsible for major successes in the fields of aerospace engineering, automotive technology, defense, and industrial systems. The function of a feedback controller is to alter the behavior of the system in order to meet a desired level of performance. Modern control techniques, whether linear or nonlinear,...
American Psychological Association, 2006. — 187 p. — ISBN: 1591474159, 978-1591474159. While the term neural networks may be unfamiliar to many organizational psychologists, exciting new applications of artificial intelligence are attracting notice among organizational behavior researchers. In Neural Networks in Organizational Research: Applying Pattern Recognition to the...
Springer, 2010. — 254 p. "Applications of Neural Networks in High Assurance Systems" is the first book directly addressing a key part of neural network technology: methods used to pass the tough verification and validation (V&V) standards required in many safety-critical applications. The book presents what kinds of evaluation methods have been developed across many sectors,...
Springer, 2002. — 289 p. The Self-Organizing Map (SOM) is one of the most frequently used architectures for unsupervised artificial neural networks. Introduced by Teuvo Kohonen in the 1980s, SOMs have been developed as a very powerful method for visualization and unsupervised classification tasks by an active and innovative community of international researchers. A number of...
North-Holland, 1991, -286 p. The first time I became interested in Neural Nets and Statistical Pattern Recognition was in early 1958 while I was a graduate student in the Moore School of Electrical Engineering of the University of Pennsylvania. My student subscription to the NEW YORKER magazine brought many chuckles from cartoons and stories but the only item from all those...
Springer Cham, 2023. — 119 p. — (SpringerBriefs in Computer Science) — eBook ISBN: 978-3-031-39179-8. This book provides a broad overview of the key results and frameworks for various NSAI tasks as well as discussing important application areas. This book also covers neuro-symbolic reasoning frameworks such as LNN, LTN, and NeurASP and learning frameworks. This would include...
Amazon Digital Services LLC, 2016. — 105 p. — ASIN B01LY27DHK. What are neural networks in a nutshell? Neural networks is a technology book that deals with both natural and our artificial lifestyle. Moreover, this guide is not just about the neural networks, but it is all about the neural networks. Neural networks have an enormous appeal to many researchers due to their great...
Amazon Digital Services LLC, 2016. — 105 p. — ASIN B01LY27DHK. What are neural networks in a nutshell? Neural networks is a technology book that deals with both natural and our artificial lifestyle. Moreover, this guide is not just about the neural networks, but it is all about the neural networks. Neural networks have an enormous appeal to many researchers due to their great...
Springer, 2016. — 468. This book covers theoretical aspects as well as recent innovative applications of Artificial Neural networks (ANNs) in natural, environmental, biological, social, industrial and automated systems. It presents recent results of ANNs in modeling small, large and complex systems under three categories, namely, 1) Networks, Structure Optimisation, Robustness...
Springer, 1997. — 155 p. This book is about training methods - in particular, fast second-order training methods - for multi-layer perceptrons (MLPs). MLPs (also known as feed-forward neural networks) are the most widely-used class of neural network. Over the past decade MLPs have achieved increasing popularity among scientists, engineers and other professionals as tools for...
Springer Cham, 2023. — 198 p. — (Synthesis Lectures on Data Mining and Knowledge Discovery). — eBook ISBN: 978-3-031-16174-2. This book provides a comprehensive introduction to the foundations and frontiers of graph neural networks. In addition, the book introduces the basic concepts and definitions in graph representation learning and discusses the development of advanced...
Wiley, 2013. — 517 p. — ISBN: 1118337840, 9781118337844 Computational Intelligence: Synergies of Fuzzy Logic, Neural Networks and Evolutionary Computing presents an introduction to some of the cutting edge technological paradigms under the umbrella of computational intelligence. Computational intelligence schemes are investigated with the development of a suitable framework for...
Basel: Birkhäuser, 1999. — 192 p. The theoretical foundations of Neural Networks and Analog Computation conceptualize neural networks as a particular type of computer consisting of multiple assemblies of basic processors interconnected in an intricate structure. Examining these networks under various resource constraints reveals a continuum of computational devices, several of...
Amazon Digital Services, Inc., 2015. — 447 p. An Excellent Book On Artificial Intelligence Programming, C++ Neural Networks And Fuzzy Logic With The Application Of C++. The number of models available in neural network literature is quite large. Very often the treatment is mathematical and complex. This book provides illustrative examples in C++ that the reader can use as a...
Nova Press, 2020. — 176 p. — ISBN: 9781536184662. As artificial neural networks have been gaining importance in the field of engineering, this compilation aims to review the scientific literature regarding the use of artificial neural networks for the modeling and optimization of food drying processes. The applications of artificial neural networks in food engineering are...
Butterworth-Heinemann, 2017. — 218 p. Diffuse Algorithms for Neural and Neuro-Fuzzy Networks: With Applications in Control Engineering and Signal Processing presents new approaches to training neural and neuro-fuzzy networks. This book is divided into six chapters. Chapter 1 consists of plants models reviews, problems statements, and known results that are relevant to the...
New York: Butterworth-Heinemann, 2017. — 215 p. Diffuse Algorithms for Neural and Neuro-Fuzzy Networks: With Applications in Control Engineering and Signal Processing presents new approaches to training neural and neuro-fuzzy networks. This book is divided into six chapters. Chapter 1 consists of plants models reviews, problems statements, and known results that are relevant to...
CreateSpace Independent Publishing, 2018. — 114 p. — ISBN: 171759445X. Are you looking to get a better understanding of neural networks and their applications? Neural networks are used to model complex patterns for prediction and simulation. It uses the processing pattern used by brain neurons to achieve this. Neural Networks are good at processing complex , non-linear...
CreateSpace Independent Publishing, 2018. — 114 p. — ISBN: 171759445X. Are you looking to get a better understanding of neural networks and their applications? Neural networks are used to model complex patterns for prediction and simulation. It uses the processing pattern used by brain neurons to achieve this. Neural Networks are good at processing complex , non-linear...
CreateSpace Independent Publishing, 2018. — 114 p. — ISBN: 171759445X. Are you looking to get a better understanding of neural networks and their applications? Neural networks are used to model complex patterns for prediction and simulation. It uses the processing pattern used by brain neurons to achieve this. Neural Networks are good at processing complex , non-linear...
Springer, 2003, -230 p. Conventional digital computation methods have run into a serious speed bottleneck due to their serial nature. To overcome this problem, a new computation model, called Neural Networks, has been proposed, which is based on some aspects of neurobiology and adapted to integrated circuits. The increased availability of computing power has not only made many...
CreateSpace Independent Publishing Platform, 2017. — 94 p. This book is an exploration of an artificial neural network. It has been created to suit even the complete beginners to artificial neural networks. The first part of the book is an overview of artificial neural networks so as to help the reader understand what they are. You will also learn the relationship between the...
CreateSpace Independent, North Charleston, USA, 2017. — 309 p. — ISBN/ASIN: B06XC2P9ZH. Predictive analytics encompasses a variety of statistical techniques from predictive modeling, machine learning, and data mining that analyze current and historical facts to make predictions about future or otherwise unknown events. In business, predictive models exploit patterns found in...
Morgan & Claypool Publ., 2017. — 241 p. — (Synthesis Lectures on Computer Architecture 39) — ISBN10: 1627059482. Understanding and implementing the brain's computational paradigm is the one true grand challenge facing computer researchers. Not only are the brain's computational capabilities far beyond those of conventional computers, its energy efficiency is truly remarkable....
Amazon Digital Services LLC, 2017. — 736 p. This book is for Java developers with basic Java programming knowledge. No previous knowledge of neural networks is required as this book covers the concepts from scratch. What You Will Learn Get to grips with the basics of neural networks and what they are used for Develop neural networks using hands-on examples Explore and code the...
New York: Springer, 2018. — 100 p. This book focuses on the fields of hybrid intelligent systems based on fuzzy systems, neural networks, bio-inspired algorithms and time series. This book describes the construction of ensembles of Interval Type-2 Fuzzy Neural Networks models and the optimization of their fuzzy integrators with bio-inspired algorithms for time series...
Handbook. — Oakville, Arcler Press, 2019. — 212 p. Applied Neural Networks and Soft Computing examines the relation between neural networks and soft computing. Neural network is a system of hardware and software designed after the operations of neurons. Applied neural networks has a plethora of applications and the text tries to touch every aspect to give readers a wider...
Arcler Press, 2020. - 260 p. - ISBN: 9781774073452 Deep neural networks and applications makes the readers aware about the various Artificial Neutral Networks (ANN) and the topologies related to Main Neutral Networks (MNN). The book throws light on the prospect of artificial intelligence and the applications it has in risk management. It further elaborates on the Artificial...
Springer, 2002. — 167 p. In the past three decades, major advances have been made in adaptive control of linear time-invariant plants with unknown parameters. The choice of the controller structure is based on well established results in linear systems theory, and stable adaptive laws which assure the global stability of the overall systems are derived based on the properties...
IGI Global, 2020. — 310 p. ISBN: 9781799835929. The field of healthcare is seeing a rapid expansion of technological advancement within current medical practices. The implementation of technologies including neural networks, multi-model imaging, genetic algorithms, and soft computing are assisting in predicting and identifying diseases, diagnosing cancer, and the examination of...
Springer, 2013, -181 p. Recent advancements in the field of telecommunications, medical imaging and signal processing deal with signals that are inherently time varying, nonlinear and complex-valued. The time varying, nonlinear characteristics of these signals can be effectively analyzed using artificial neural networks. Furthermore, to efficiently preserve the physical...
Springer, 1996, -316 p. The topic of this book is the use of artificial neural networks for modeling and control purposes. The relatively young field of neural control, which started approximately ten years ago with Barto's broomstick balancing experiments, has undergone quite a revolution in recent years. Many methods emerged including optimal control, direct and indirect...
InTech, 2013, -264 p. Artificial neural networks may probably be the single most successful technology in the last two decades which has been widely used in a large variety of applications in various areas. An artificial neural network, often just called a neural network, is a mathematical (or computational) model that is inspired by the structure and function of biological...
InTech, 2011. — 490 p. Artificial neural networks may probably be the single most successful technology in the last two decades which has been widely used in a large variety of applications in various areas. An artificial neural network, oft en just called a neural network, is a mathematical (or computational) model that is inspired by the structure and function of biological...
InTech, 2011, -374 p. Artificial neural networks may probably be the single most successful technology in the last two decades which has been widely used in a large variety of applications in various areas. An artificial neural network, often just called a neural network, is a mathematical (or computational) model that is inspired by the structure and function of biological...
Morgan & Claypool Publishers, 2020. — 341 p. — (Synthesis Lectures on Computer Architecture). — ISBN: 1681738317. This book provides a structured treatment of the key principles and techniques for enabling efficient processing of deep neural networks (DNNs). DNNs are currently widely used for many artificial intelligence (AI) applications, including computer vision, speech...
CRC Press, 2014. — 298 p. — ISBN: 1482233398, 9781482233391. The utility of artificial neural network models lies in the fact that they can be used to infer functions from observations — making them especially useful in applications where the complexity of data or tasks makes the design of such functions by hand impractical. Exploring Neural Networks with C# presents the...
World Scientific, 1996, -236 p. Since Hopfield proposed neural-network computing for optimization and combinatorics problems, many neural-network investigators have been working on optimization problems. In this book, a variety of optimization problems and combinatorics problems are presented by respective experts. The book contains applications in graph theory, mathematics,...
Springer, 2007. — 309 p. Neural Networks: Computational Models and Applications covers a wealth of important theoretical and practical issues in neural networks, including the learning algorithms of feed-forward neural networks, various dynamical properties of recurrent neural networks, winner-take-all networks and their applications in broad manifolds of computational...
Elsevier, 1998. — 151 p. Neural networks have shown enormous potential for commercial exploitation over the last few years but it is easy to overestimate their capabilities. A few simple algorithms will learn relationships between cause and effect or organise large volumes of data into orderly and informative patterns but they cannot solve every problem and consequently their...
Academic Press, 2020. — 281 p. — ISBN: 978-0-12-815651-3. This book presents a new approach on how to quickly construct an accurate, multilayered neural network solution of differential equations. Current neural network methods have significant disadvantages, including a lengthy learning process and single-layered neural networks built on the finite element method (FEM). The...
Springer, 2013. — 285 p. Themain purpose of the book is to present, through material based on a collection of relevant past scientific papers by the author, a complete development of the CODAM neural network model of consciousness, first introduced by the author in 1999/2000. This model allows both for a scientifically based (and scientifically supported) understanding of...
Blue Windmill Media, 2017. — 248 p. Neural networks have made a gigantic comeback in the last few decades and you likely make use of them everyday without realizing it, but what exactly is a neural network? What is it used for and how does it fit within the broader arena of machine learning? On a high level, a network learns just like we do, through trial and error. This is...
Blue Windmill Media, 2017. — 248 p. Neural networks have made a gigantic comeback in the last few decades and you likely make use of them everyday without realizing it, but what exactly is a neural network? What is it used for and how does it fit within the broader arena of machine learning? On a high level, a network learns just like we do, through trial and error. This is...
Blue Windmill Media, 2017. — 248 p. Neural networks have made a gigantic comeback in the last few decades and you likely make use of them everyday without realizing it, but what exactly is a neural network? What is it used for and how does it fit within the broader arena of machine learning? On a high level, a network learns just like we do, through trial and error. This is...
Independently published, 2017. — 167 p. — ISBN: 9781549893643. There are many reasons why neural networks fascinate us and have captivated headlines in recent years. They make web searches better, organize photos, and are even used in speech translation. Heck, they can even generate encryption. At the same time, they are also mysterious and mind-bending: how exactly do they...
Independently published, 2017. — 167 p. — ISBN: 9781549893643. There are many reasons why neural networks fascinate us and have captivated headlines in recent years. They make web searches better, organize photos, and are even used in speech translation. Heck, they can even generate encryption. At the same time, they are also mysterious and mind-bending: how exactly do they...
Springer, 2012. — 140 p. Sloshing causes liquid to fluctuate, making accurate level readings difficult to obtain in dynamic environments. The measurement system described uses a single-tube capacitive sensor to obtain an instantaneous level reading of the fluid surface, thereby accurately determining the fluid quantity in the presence of slosh. A neural network based...
Springer, 2002. — 215 p. This book revives, analyzes, and simulates Turing's ideas, applying them to different types of problems, and building and training Turing's machines using evolutionary algorithms. In a little known paper entitled 'Intelligent Machinery' Turing investigated connectionist networks, but his work was dismissed as a 'schoolboy essay'and it was left...
Kluwer, 1994. — 274. This book is the result of an attempt to broaden the scope of machine learning. The framework proposed here, called lifelong learning, addresses scenarios in which a learning algorithm faces a whole collection of learning tasks. Instead of having just an isolated set of data points, a lifelong learning algorithm can incrementally build on previous learning...
Birkhäuser, 2006. — 181 p. This book describes the results of a successful collaboration between a group at the Department of Physics of Rome University “La Sapienza” and a group of engineers who for many years have been engaged in researching marine events at the National Department of Technical Services of Italy (“Dipartimento dei Servizi Tecnici Nazionali, DSTN, now APAT,...
Academic Press, 2019. — 324 p. — ISBN: 978-0-12-815254-6. This book presents a new approach on how to obtain the adaptive neural network models for complex systems that are typically found in real-world applications. The book introduces the theoretical knowledge available for the modeled system into the purely empirical black box model, thereby converting the model to the gray...
Cambridge University Press, 2010, -409 p. This book represents a substantial update of a theme issue of the Philosophical Transactions of the Royal Society B Journal, ‘The use of artificial neural networks to study perception in animals’ (Phil Trans R Soc B 2007 March 29; 362(1479)). Most of the 14 papers in that theme issue have been significantly updated and we include a...
Springer, 2015, -179 p. The neurocomputing is endowed with an opportunity to realize the innate enigma of artificial intelligence in physical world. The researchers of modern computing science have sought to see the neurocomputing as cutting edge technology in computational intelligence and machine learning. The goal of this book is to bring in the elegant theory of...
Irwin Professional Pub, 1996. — 865 p. — ISBN: 1557389195, 9781557389190 This book is an excellent book on the application of Neural networks to the financial world. This is a collection of articles for the knowledgeable in the area of financial investments and Neural Networks. With this in mind, this book constitutes an excellent reference book to any graduate course in any...
CRC Press, 2022. — 330 p. — ISBN: 978-0-367-69838-6. The brain is not a glorified digital computer. It does not store information in registers, and it does not mathematically transform mental representations to establish perception or behavior. The brain cannot be downloaded to a computer to provide immortality, nor can it destroy the world by having its emerged consciousness...
CRC Press, 2022. — 412 p. — ISBN: 9781003307822. The text discusses recurrent neural networks for prediction and offers new insights into the learning algorithms, architectures, and stability of recurrent neural networks. It discusses important topics including recurrent and folding networks, long short-term memory (LSTM) networks, gated recurrent unit neural networks, language...
Springer, 2002, -424 p. The fourth volume of the Physics of Neural Networks series. Close this book for a moment and look around you. You scan the scene by directing your attention, and gaze, at certain specific objects. Despite the background, you discern them. The process is partially intentional and partially preattentive. How all this can be done is described in the fourth...
Springer, 2021. — 276 p. — ISBN: 3662611821, 9783662611821. This book treats essentials from neurophysiology (Hodgkin–Huxley equations, synaptic transmission, prototype networks of neurons) and related mathematical concepts (dimensionality reductions, equilibria, bifurcations, limit cycles and phase plane analysis). This is subsequently applied in a clinical context, focusing...
Prentice Hall, 1995. - 259 p. Thorough, compact, and self-contained, this explanation and analysis of a broad range of neural nets is conveniently structured so that readers can first gain a quick global understanding of neural nets - "without" the mathematics - and can then delve into mathematical specifics as necessary. The behavior of neural nets is first explained from an...
CRC Press, 2017. — 188 p. This book covers the fundamentals in designing and deploying techniques using deep architectures. It is intended to serve as a beginner’s guide to engineers or students who want to have a quick start on learning and/or building deep learning systems. This book provides a good theoretical and practical understanding and a complete toolkit of basic...
Springer, 2003. — 498 p. Learning and Generalization provides a formal mathematical theory for addressing intuitive questions such as: how does a machine learn a new concept on the basis of examples; how can a neural network, after sufficient training, correctly predict the outcome of a previously unseen input; how much training is required to achieve a specified level of...
Academic Press, 2021. — 526 p. — ISBN: 978-3-030-74651-3. Mem-elements for Neuromorphic Circuits with Artificial Intelligence Applications illustrates recent advances in the field of mem-elements (memristor, memcapacitor, meminductor) and their applications in nonlinear dynamical systems, computer science, analog and digital systems, and in neuromorphic circuits and artificial...
ITexLi, 2024. — 88 p. — ISBN: 0854661514 9780854661510 0854661522 9780854661527 0854661530 9780854661534. This book presents some recent specialized works of a theoretical study in the domain of adaptive neuro-fuzzy inference systems (ANFIS) for specialists, engineers, professors, and students. It includes five chapters that present new fuzzy systems concepts and promote them...
World Scientific, 1999, -194 p. This book presents our research on the application of evolutionary computation in the automatic generation of a neural network architecture. The architecture has a significant influence on the performance of the network. It is the usual practice to use trial and error to find a suitable neural network architecture for a given problem. This method...
Springer, 2021. — 197 p. — ISBN: 978-3-030-75648-2. The aim of this publication is to identify and apply suitable methods for analyzing and predicting the time series of gold prices, together with acquainting the reader with the history and characteristics of the methods and with the time series issues in general. Both statistical and econometric methods, and especially...
New York: Springer, 2017. — 225 p. Up to date, many researchers have devoted much effort to dynamical behaviors for coupled neural networks (CNNs) because of their wide applications in different fields. For instance, the CNNs have been triumphantly applied to harmonic oscillation generation, chaos generators design, secure communication, the electronic circuits, and memorizing...
Springer, 2016. – 398 p. Background of This Book With the development of neural networks theory, many neural network models and stability concepts have been extended and upgraded. For example, it is well known that recurrent neural networks (RNNs) can be used to realize associate memory and information storage. The fundamental explanation of the statement is based on the fact...
CRC Press, 2022. — 240 p. — ISBN13: 9781003327219. Filter Design for System Modeling, State Estimation, and Fault Diagnosis analyzes the latest methods in the design of filters for system modeling, state estimation, and fault detection to provide a new perspective on both theoretical and practical aspects. This book also includes fault diagnosis techniques for unknown but...
O’Reilly Media, 2019. — 247 p. — ISBN: 978-1-492-04495-6. As deep neural networks (DNNs) become increasingly common in real-world applications, the potential to deliberately "fool" them with data that wouldn’t trick a human presents a new attack vector. This practical book examines real-world scenarios where DNNs — the algorithms intrinsic to much of AI — are used daily to...
O’Reilly Media, 2019. — 247 p. — ISBN: 978-1-492-04495-6. As deep neural networks (DNNs) become increasingly common in real-world applications, the potential to deliberately “fool” them with data that wouldn’t trick a human presents a new attack vector. This practical book examines real-world scenarios where DNNs — the algorithms intrinsic to much of AI — are used daily to...
O’Reilly Media, 2019. — 228 p. — ISBN: 978-1-492-04495-6. As deep neural networks (DNNs) become increasingly common in real-world applications, the potential to deliberately “fool” them with data that wouldn’t trick a human presents a new attack vector. This practical book examines real-world scenarios where DNNs — the algorithms intrinsic to much of AI — are used daily to...
O’Reilly Media, 2019. — 228 p. — ISBN: 978-1-492-04495-6. As deep neural networks (DNNs) become increasingly common in real-world applications, the potential to deliberately “fool” them with data that wouldn’t trick a human presents a new attack vector. This practical book examines real-world scenarios where DNNs — the algorithms intrinsic to much of AI — are used daily to...
O’Reilly Media, 2019. — 228 p. — ISBN: 978-1-492-04495-6. As deep neural networks (DNNs) become increasingly common in real-world applications, the potential to deliberately “fool” them with data that wouldn’t trick a human presents a new attack vector. This practical book examines real-world scenarios where DNNs — the algorithms intrinsic to much of AI — are used daily to...
Springer, 2001. — 587 p. It is generally understood that the present approachs to computing do not have the performance, flexibility, and reliability of biological information processing systems. Although there is a comprehensive body of knowledge regarding how information processing occurs in the brain and central nervous system this has had little impact on mainstream...
Singapore: Springer Singapore, 2019. — 132 p. — ISBN: 978-981-13-0062-2. This book offers an introduction to modern natural language processing using machine learning, focusing on how neural networks create a machine interpretable representation of the meaning of natural language. Language is crucially linked to ideas - as Websters 1923 English Composition and Literature puts...
Wiley, 2018. — 271 p. — (Wiley series on cooperative communications). — ISBN: 978-1-119-21599-8 Autonomic Intelligence Evolved Cooperative Networking offers a comprehensive advancement of the state-of-the art technological developments in the fields of Cooperative Networking and Autonomic Computing. Based on his track record in industrial standardisation, as well as academic...
Elsevier, 2000, -219 p. The resurgence of interest in artificial neural networks fortunately coincided with the emergence of new technology in molecular biology and the explosion of information about the genomes of humans and other species. Many important problems in genome informatics have been successfully addressed with artificial neural networks, and a vast literature has...
Elsevier, 2000, -207 p. The resurgence of interest in artificial neural networks fortunately coincided with the emergence of new technology in molecular biology and the explosion of information about the genomes of humans and other species. Many important problems in genome informatics have been successfully addressed with artificial neural networks, and a vast literature has...
Singapore: Springer, 2022. — 701 p. Deep Learning models are at the core of artificial intelligence research today. It is well known that deep learning techniques are disruptive for Euclidean data, such as images or sequence data, and not immediately applicable to graph-structured data such as text. This gap has driven a wave of research for deep learning on graphs, including...
Wiley-VCH, 2023. — 435 p. — ISBN: 978-1-119-98599-0. Describes the theoretical and practical aspects of finite-time ZNN methods for solving an array of computational problems. Zeroing Neural Networks (ZNN) have become essential tools for solving discretized sensor-driven time-varying matrix problems in engineering, control theory, and on-chip applications for robots. Building...
Wiley-VCH, 2023. — 435 p. — ISBN: 978-1-119-98599-0. Zeroing Neural Networks. Describes the theoretical and practical aspects of finite-time ZNN methods for solving an array of computational problems. Zeroing Neural Networks (ZNN) have become essential tools for solving discretized sensor-driven time-varying matrix problems in engineering, control theory, and on-chip...
Springer, 2021. — 233 p. — (Lecture Notes on Numerical Methods in Engineering and Sciences). — ISBN: 978-3030661106. This book shows how neural networks are applied to computational mechanics. Part I presents the fundamentals of neural networks and other machine learning method in computational mechanics. Part II highlights the applications of neural networks to a variety of...
World Scientific, 2005, -247 p. Since Poincare's work on the three body problem, the study of nonlinear phenomena has received considerable attention in various disciplines, which made the subject of nonlinear dynamics a topic of wide interdisciplinary interest. These efforts have nowadays provided a new perspective to engineers for designing new information processing systems....
Springer, 2023. — 339 p. — eBook ISBN: 978-981-99-1790-7. Introduces readers to a modern theory of the minimum description length (MDL) principle. Includes rich examples of MDL applications to machine learning and data science. Written by a pioneer of information-theoretic learning theory This book introduces readers to the minimum description length (MDL) principle and its...
Independently published, 2021. — 186 p. — (Herong's Tutorial Examples). — ISBN: 979-8720214708. This book is a collection of notes and sample codes written by the author while he was learning Neural Networks in Machine Learning. Topics include Neural Networks (NN) concepts: nodes, layers, activation functions, learning rates, training sets, etc.; deep playground for classical...
Springer Cham, 2024. — 239 p. — (Synthesis Lectures on Engineering, Science, and Technology). — eBook ISBN: 978-3-031-57873-1. Discusses neuromorphic intelligence, which serves as a foundation for compact, low-power brain-inspired systems. Includes learning algorithms, architecture design, and implementation of large-scale systems. Offers a holistic view of the field, allowing...
Elsevier, 2017. — 167 p. — ISBN: 978-0-12-811654-8. This book provides the principle knowledge of temporal data mining in association with unsupervised ensemble learning and the fundamental problems of temporal data clustering from different perspectives. By providing three proposed ensemble approaches of temporal data clustering, this book presents a practical focus of...
Prentice-Hall, 2005, -479 p. Over the past fifteen years, a view has emerged that computing based on models inspired by our understanding of the structure and function of the biological neural networks may hold the key to the success of solving intelligent tasks by machines. The new field is called Artificial Neural Networks, although it is more apt to describe it as parallel...
ITexLi, 2023. — 272 p. — ISBN: 1803561440 9781803561448 1803561432 9781803561431 1803561459 9781803561455. Dive into the cutting-edge world of Neuromorphic Computing, a groundbreaking volume that unravels the secrets of brain-inspired computational paradigms. Spanning neuroscience, artificial intelligence, and hardware design, this book presents a comprehensive exploration of...
Springer, 2004. — 244 p. Since the outstanding and pioneering research work of Hopfield on recurrent neural networks (RNNs) in the early 80s of the last century, neural networks have rekindled strong interests in scientists and researchers. Recent years have recorded a remarkable advance in research and development work on RNNs, both in theoretical research as weIl as actual...
Springer, 2003, -323 p. The foreign exchange market is one of the most complex dynamic markets with the characteristics of high volatility, nonlinearity and irregularity. Since the Bretton Woods System collapsed in 1970s, the fluctuations in the foreign exchange market are more volatile than ever. Furthermore, some important factors, such as economic growth, trade development,...
Springer, 2017. — 267 p. — ISBN10: 3319543121. — ISBN13: 978-3319543123. This book summarizes the recent breakthroughs in hardware implementation of neuro-inspired computing using resistive synaptic devices. The authors describe how two-terminal solid-state resistive memories can emulate synaptic weights in a neural network. Readers will benefit from state-of-the-art summaries...
World Scientific, 2003, -510 p. The field of Artificial Neural Networks (ANN) represents an emerging design method still dominated by research. Despite the fact that there is much more research to be done before it becomes a fully accepted and established engineering discipline it is finding application in an ever- growing number of real-world problems. Enough fundamental...
Springer, 2020. — 527 p. — ISBN: 3030484521. This book is intended for specialists as well as students and graduate students in the field of artificial intelligence, robotics and information technology. It is will also appeal to a wide range of readers interested in expanding the functionality of artificial intelligence systems. One of the pressing problems of modern artificial...
Springer Singapore, 2024. — 260 p. — (Computational Intelligence Methods and Applications). — eBook ISBN: 978-981-99-5068-3. Review recent advances in CNN compression and acceleration. Elaborate on recent advances in deep model compression technologies. Introduce applications of model compression in image classification, speech recognition, object detection, etc.. Deep learning...
CRC Press, 2023. — 215 p. — eBook ISBN: 978-1-003-37613-2. Deep learning has achieved impressive results in image classification, computer vision, and natural language processing. To achieve better performance, deeper and wider networks have been designed, which increase the demand for computational resources. The number of floating point operations (FLOPs) has increased...
Berlin/Boston: De Gruyter, 2018. — 296 p. — ISBN: 3110449625. The book begins with an introduction of blind equalization theory and its application in neural networks, then discusses the algorithms in recurrent networks, fuzzy networks and other frequently-studied neural networks. Each algorithm is accompanied by derivation, modeling and simulation, making the book an essential...
IGI Global, 2009. — 542 p. Artificial Neural Networks (ANNs) are known to excellence in pattern recognition, pattern matching and mathematical function approximation. However, they suffer from several limitations. ANNs are often stuck in local, rather than global minima, as well as taking unacceptable long times to converge in the real word data. Especially from the perspective...
IGI Global, 2016. — 511 p. — ISBN: 978-1-5225-0064-3. In recent years, Higher Order Neural Networks (HONNs) have been widely adopted by researchers for applications in control signal generating, pattern recognition, nonlinear recognition, classification, and prediction of control and recognition scenarios. Because HONNs have been proven to be faster, more accurate, and easier...
Williston, USA, Morgan & Claypool, 2018. — 90 p. — (Synthesis Lectures on Communications 13). — ISBN10: 1681732920. The area of detection and estimation in a distributed wireless sensor network (WSN) has several applications, including military surveillance, sustainability, health monitoring, and Internet of Things (IoT). Compared with a wired centralized sensor network, a...
Springer, 2000. — 369 p. People are facing more and more NP-complete or NP-hard problems of combinatorial nature and of a continuous nature in economic, military and management practice. There are two ways in which one can enhance the efficiency of searching for the solutions of these problems. The first is to improve the speed and memory capacity of hardware. We all have...
New York: Chapman and Hall/CRC, 2019. — 369 p. Toward Deep Neural Networks: WASD Neuronet Models, Algorithms, and Applications introduces the outlook and extension toward deep neural networks, with a focus on the weights-and-structure determination (WASD) algorithm. Based on the authors’ 20 years of research experience on neuronets, the book explores the models, algorithms, and...
Springer, 2022. — 235 p. — (Intelligent Control and Learning Systems 4). — ISBN: 9811954496. This book provides up-to-date developments in the stability analysis and (anti-)synchronization control area for complex-valued neural network systems with time delay. It brings out the characteristic systematism in them and points out further insight to solve relevant problems. It...
New York: Springer, 2021. — 120 p. Neural networks as the commonly used machine learning algorithms, such as artificial neural networks (ANNs) and convolutional neural networks (CNNs), have been extensively used in the GIScience domain to explore the nonlinear and complex geographic phenomena. However, there are a few studies that investigate the parameter settings of neural...
New York: Wiley-IEEE Press, 2020. — 289 p. Explains current co-design and co-optimization methodologies for building hardware neural networks and algorithms for machine learning applications. Machine learning, especially deep learning, has emerged as an important discipline through which many conventionally difficult problems, such as pattern recognition, decision making, and...
Wiley-IEEE Press, 2020. — 290 p. — ISBN: 978-1-119507390. Explains current co-design and co-optimization methodologies for building hardware neural networks and algorithms for machine learning applications This book focuses on how to build energy-efficient hardware for neural networks with learning capabilities — and provides co-design and co-optimization methodologies for...
Springer, 2016. – 367 p. The past few decades have witnessed the successful application of neural networks in many areas such as image processing, pattern recognition, associative memory, and optimization problems. For neural networks dynamics, the state variables of the model are the output signals of the neurons, and a steady output is needed in the dynamical evolution of...
Springer, 2024. — 439 p. — (Springer Series on Bio- and Neurosystems 16). — ISBN: 978-3-031-36705-2. This book offers a timely and comprehensive review of the field of neurotronics. Gathering cutting-edge contributions from neuroscientists, biologists, psychologists, as well as physicists, microelectronics engineers, and information scientists, it gives extensive information on...
Springer, 2024. — 439 p. — (Springer Series on Bio- and Neurosystems 16). — ISBN: 978-3-031-36704-5. This book offers a timely and comprehensive review of the field of neurotronics. Gathering cutting-edge contributions from neuroscientists, biologists, psychologists, as well as physicists, microelectronics engineers, and information scientists, it gives extensive information on...
New York: River Publishers , 2019. — 266 p. This volume presents novel real-time, reconfigurable, multi-chip spiking neural network system architecture based on localized communication, which effectively reduces the communication cost to a linear growth. The system use double floating-point arithmetic for the most biologically accurate cell behavior simulation, and is flexible...
Nova Science Publishers, 2017. — 365 p. — ISBN: 978-1536121148. With today's growing information and the overloading of its volume, it is becoming tremendously difficult to analyze the huge amounts of data that contain the information and which makes it very strenuous and inconvenient to introduce an appropriate methodology of decision making fast enough to the point that it...
New York: Springer. 2020. — 532 p. The Basic Infinite-Dimensional or Functional Optimization Problem IDO and FDO Problems From the Ritz Method to the Extended Ritz Method (ERIM) Approximation of Functions From Function Approximation to Approximate Infinite-Dimensional Optimization Relationships with Parametrized Control Approaches Contents and Structure of the Book Statement of...
West Publishing, 1992, -765 p. The recent resurgence of interest in neural networks has its roots in the recognition that the brain performs computations in a different manner than do conventional digital computers. Computers are extremely fast and precise at executing sequences of instructions that have been formulated for them. A human information processing system is...
West Publishing, 1992. — 764 p. — ISBN: 0-314-93391-3. The recent resurgence of interest in neural networks has its roots in the recognition that the brain performs computations in a different manner than conventional digital computers. Computers are extremely fast and precise at executing sequences of instructions that have been formulated for them. A human information...
Springer, 2014. — 336 p. This book thoroughly discusses computationally efficient (suboptimal) Model Predictive Control (MPC) techniques based on neural models. The subjects treated include: A few types of suboptimal MPC algorithms in which a linear approximation of the model or of the predicted trajectory is successively calculated on-line and used for prediction....
Springer, 2022. — 358 p. — ISBN: 978-3-030-83814-0. Nonlinear Predictive Control Using Wiener Models: Computationally Efficient Approaches for Polynomial and Neural Structures This book presents computationally efficient MPC solutions. The classical model predictive control (MPC) approach to control dynamical systems described by the Wiener model uses an inverse static block to...
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