Flexible Neuro-Fuzzy Systems: Structures, Learning and Performance Evaluation (The Springer International Series in Engineering and Computer Science)

Rutkowski, Leszek

ISBN 10: 1402080425 / ISBN 13: 9781402080425

Published by Springer, 2004

About this Item

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Bibliographic Details

• Title: Flexible Neuro-Fuzzy Systems: Structures, Learning and Performance Evaluation (The Springer International Series in Engineering and Computer Science)
• Publisher: Springer
• Publication Date: 2004
• Language: English
• ISBN 10: 1402080425
• ISBN 13: 9781402080425
• Binding: Hardcover
• Condition: As New

About this title

Synopsis

Flexible Neuro-Fuzzy Systems is the first professional literature about the new class of powerful, flexible fuzzy systems. The author incorporates various flexibility parameters to the construction of neuro-fuzzy systems. This approach dramatically improves their performance, allowing the systems to perfectly represent the pattern encoded in data.
Flexible Neuro-Fuzzy Systems is the only book that proposes a flexible approach to fuzzy modeling and fills the gap in existing literature. This book introduces new fuzzy systems which outperform previous approaches to system modeling and classification, and has the following features:

-Provides a framework for unification, construction and development of neuro-fuzzy systems;
-Presents complete algorithms in a systematic and structured fashion, facilitating understanding and implementation,
-Covers not only advanced topics but also fundamentals of fuzzy sets,
-Includes problems and exercises following each chapter,
-Illustrates the results on a wide variety of simulations,
-Provides tools for possible applications in business and economics, medicine and bioengineering, automatic control, robotics and civil engineering.

Review

From the reviews:

"This research monograph offers a very detailed insight into ... flexible neurofuzzy architectures. ... The author systematically guides the reader through the essence of the neurofuzzy systems. ... Careful attention to details along with well-organized and systematic derivations of all learning formulas are the important features of the book. The wealth of experimental material and thorough comparative analysis is another strength of the book. The list of references is very much updated ... . Overall, the book is worth studying." (Witold Pedrycz, Zentralblatt MATH, Vol. 1080, 2006)

"It is fair to say that the work presented in this book moves the whole area of modeling by neuro-fuzzy systems to a higher level. ... a great resource for researchers in this area, who have a fairly strong background in both fuzzy systems and artificial neural networks. It is also an excellent textbook for advanced graduate students in engineering, systems science, computer science, information science, and perhaps some other areas, who are interested in neuro-fuzzy systems and, more generally, in applications of fuzzy mathematics." (George J. Klir, International Journal of General Systems, Vol. 34 (3), 2005)