Flexible Neuro-Fuzzy Systems - Structures, Learning and Performance Evaluation (Paperback, Softcover reprint of the original 1st ed. 2004)

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.

General

Imprint:	Springer-Verlag New York
Country of origin:	United States
Series:	The Springer International Series in Engineering and Computer Science, 771
Release date:	April 2013
First published:	2004
Authors:	Leszek Rutkowski
Dimensions:	235 x 155 x 16mm (L x W x T)
Format:	Paperback
Pages:	279
Edition:	Softcover reprint of the original 1st ed. 2004
ISBN-13:	978-1-4757-7932-5
Categories:	Books > Reference & Interdisciplinary > Communication studies > Information theory > Cybernetics & systems theory Books > Science & Mathematics > Mathematics > Mathematical foundations > General Books > Computing & IT > Applications of computing > Artificial intelligence > Neural networks
LSN:	1-4757-7932-1
Barcode:	9781475779325

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