Here is the listing of Best reference books on Neural Network and Fuzzy Logic.
|1. “Neural Networks A Comprehensive Foundation” by Simon Haykin
Book Review: The various topics covered in the book are feedforward networks, back propagation, self-organizing maps, PCA and hierarchical machines. This book being highly inspired by biology models brain function and structure. There are many network models like adaptive resonance theory, BCS/FCS, integrate and fire models and many more. There are many applications like pattern recognition, image processing, clustering and this book is very useful to graduates and advanced undergraduates.
|2. “Artificial Neural Networks” by B. Yegnanarayana
Book Review: This book explains the fundamentals of computing with models of artificial neural networks and is very useful for practicing engineers and research scientists. This book presents the emerging and exciting areas of artificial neural networks with many features. The book stresses on pattern processing features of neural networks. The book also includes real world applications of neural networks in speech and image processing. The topics included in the book are activation and synaptic dynamics, learning laws and analysis of feedforward and feedback neural networks and complex pattern recognition architectures.
|3. “Fuzzy Logic with Engineering Applications” by Timothy. J. Ross
Book Review: This book covers the latest advancements in the field of fuzzy logic with the inclusion of MLFE methods using genetic algorithms, cognitive mapping, fuzzy agent based models and total uncertainty. The book eliminates redundant topics and is directed towards many methods and techniques. Every chapter in the book has been revised thereby including new illustrations and examples throughout. The other chapters included in the book are basic operations, membership function generation and specialized applications.
|4. “Introduction to Artificial Neural Systems” by Jacek M. Zurada
Book Review: The author explains all the concepts in very simple terminologies. The author has covered all the learning algorithms with their advantages, disadvantages and their stability. This is a very important book for neural engineers because it covers the entire subject in depth. The book helps the readers to very nicely implement the ideas in software.
|5. “Neural Network Fundamentals with Graphs, Algorithms and applications” by N.K. Bose, P.Liang
Book Review: This book is suitable for students at the graduate and undergraduate levels in the field of neural networks and neurocomputing. The book presents neural network theory for many applications in a unique manner. The structures of artificial neural networks in the book are distinguished by various classes of graphs. The book also covers the concepts of artificial intelligence, intelligent systems along with many examples, exercises and graphics.
Sanfoundry Global Education & Learning Series – Best Reference Books!