Neuro-Fuzzy and Soft Computing: A Computational Approach to Learning and Machine Intelligence

Jang, Jyh-Shing Roger; Sun, Chuen-Tsai; Mizutani, Eiji

ISBN 10: 0132610663 / ISBN 13: 9780132610667

Editore: Pearson, 1997

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Missing dust jacket; Pages can have notes/highlighting. Spine may show signs of wear. ~ ThriftBooks: Read More, Spend Less 2.5. Codice articolo G0132610663I3N01

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Titolo: Neuro-Fuzzy and Soft Computing: A ...

Casa editrice: Pearson

Data di pubblicazione: 1997

Legatura: Paperback

Condizione: Good

Condizione sovraccoperta: No Jacket

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Riassunto:

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.

Product Description:

This text provides a comprehensive treatment of the constituent methodologies underlying neuro-fuzzy and soft computing, an evolving branch within the scope of computational intelligence that is drawing increasingly more attention as it develops. The book places particular emphasis on theoretical aspects of covered methodologies, as well as empirical observations and verifications of various applications in practice. It is organized into seven sections: introduction, overview of system identification and optimization techniques, introduction of important neural network paradigms found in the literature, explanations for building ANFIS and CANFIS, structure identification techniques for neural networks and fuzzy modeling, approaches to the design of neuro-fuzzy controllers, and application examples in different domains.