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...
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...
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...
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....
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...
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...
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, 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...
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...
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...
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:...
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...
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...
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...
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...
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,...
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....
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...
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...
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...
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...
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...
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...
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,...
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...
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...
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...
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...
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...
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...
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...
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...
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...
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...
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...
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...
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...
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...
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...
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...
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