Alavala C.R. Fuzzy Logic and Neural Networks. Basic Concepts & Applications

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 research interests of the readers.
Modeling and control of dynamic systems belong to the fields in which fuzzy set techniques have received considerable attention, not only from the scientific community but also from industry. Many systems are not amenable to conventional mode ling approaches due to the lack of precise, formal knowledge about the system, due to strongly non-linear behaviour, due to the high degree of uncertainty, or due to the time varying characteristics. Fuzzy modeling along with other related techniques such as neural networks have been recognized as powerful tools, which can facilitate the effective development of models.
The approach adopted in this book aims at the development of transparent rule-based fuzzy models which can accurately predict the quantities of interest and at the same time provide insight into the system that generated the data. Attention is paid to the selection of appropriate model structures in terms of the dynamic properties, as well as the internal structure of the fuzzy rules.
The field of neural networks has a history of some five decades but has found solid application only in the past fifteen years, and the field is still developing rapidly. Thus, it is distinctly different from the fields of control systems or optimization where the terminology, basic mathematics, and design procedures have been firmly established and applied for many years. Neural networks are useful for industry, education and research. This book is intended to cover widely primarily the topics on neural computing, neural modeling, neural learning, and neural memory.
Recently, a great deal of research activity has focused on the development of methods to build or update fuzzy models from numerical data. Most approaches are based on neuro-fuzzy systems, which exploit the functional similarity between fuzzy reasoning systems and neural networks. This combination of fuzzy systems and neural networks enables a more effective use of optimization techniques for building fuzzy systems, especially with regard to their approximation accuracy. Neuro-fuzzy models can be regarded as black-box models, which provide little insight to help understand the underlying process.
The orientation of the book is towards methodologies that in the author's experience proved to be practically useful. The presentation reflects theoretical and practical issues in a balanced way, aiming at readership from the academic world and also from industrial practice. Examples are given throughout the text and six selected real-world applications are presented in detail.Fuzzy Sets and Fuzzy Logic
Fuzzy Relations
Fuzzy Implications
The Theory of Approximate Reasoning
Fuzzy Rule-Based Systems
Fuzzy Reasoning Schemes
Fuzyy Logic Controllers
Fuzzy Logic Applications
Neural Networks Fundamentals
Perceptron and Adaline
Back-Propagation
Recurrent Networks
Self-Organizing Networks
Reinforcement Learning
Neural Networks Applications
Hybrid Fuzzy Neural Networks
Hybrid Fuzzy Neural Networks Applications