Neuro-Fuzzy And Soft Computing – Jyh and Shing Roger Jang

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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 of which involve MATLAB programming tasks to provide readers with hands-on programming experiences for practical problem-solving. Each chapter also includes a reference list to the research literature so that readers may pursue topics in greater depth. This book is suitable as a self-study guide by researchers who want to learn basic and advanced neuro-fuzzy and soft computing within the framework of computational intelligence.

From the Publisher

This text provides the first comprehensive treatment of the methodologies underlying neuro-fuzzy and soft computing, an evolving branch within the scope of computational intelligence. The book places equal emphasis on theoretical aspects of covered methodologies, empirical observations and verifications of various applications in practice.

From the Back Cover

Neuro-Fuzzy and Soft Computing provides the first comprehensive treatment of the constituent methodologies underlying neuro-fuzzy and soft computing, an evolving branch of computational intelligence. The constituent methodologies include fuzzy set theory, neural networks, data clustering techniques, and several stochastic optimization methods that do not require gradient information. In particular, the authors put equal emphasis on theoretical aspects of covered methodologies, as well as empirical observations and verifications of various applications in practice. The book is well suited for use as a text for courses on computational intelligence and as a single reference source for this emerging field. To help readers understand the material the presentation includes more than 50 examples, more than 150 exercises, over 300 illustrations, and more than 150 Matlab scripts. In addition, Matlab is utilized to visualize the processes of fuzzy reasoning, neural-network learning, neuro-fuzzy integration and training, and gradient-free optimization (such as genetic algorithms, simulated annealing, random search, and downhill Simplex method). The presentation also makes use of SIMULINK for neuro-fuzzy control system simulations. All Matlab scripts used in the book are available on the free companion software disk that may be ordered by using the enclosed reply card. The book also contains an “Internet Resource Page” to point the reader to on-line neuro-fuzzy and soft computing home pages, publications, public-domain software, research institutes, news groups, etc. All the HTTP and FTP addresses are available as a bookmark file on the companion software disk.

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