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Compensatory genetic fuzzy neural networks and their applications /

This book presents a powerful hybrid intelligent system based on fuzzy logic, neural networks, genetic algorithms and related intelligent techniques. The new compensatory genetic fuzzy neural networks have been widely used in fuzzy control, nonlinear system modeling, compression of a fuzzy rule base...

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Bibliographic Details
Main Authors: Zhang, Yan-Qing
Corporate Authors: World Scientific Firm
Group Author: Kandel, Abraham
Published: World Scientific Pub. Co.,
Publisher Address: Singapore ; River Edge, N.J. :
Publication Dates: 1998.
Literature type: eBook
Language: English
Series: Series in machine perception and artificial intelligence ; vol. 30
Subjects:
Neural networks Computer science
Fuzzy systems
Electronic books
Online Access: http://www.worldscientific.com/worldscibooks/10.1142/3678#t=toc
Summary: This book presents a powerful hybrid intelligent system based on fuzzy logic, neural networks, genetic algorithms and related intelligent techniques. The new compensatory genetic fuzzy neural networks have been widely used in fuzzy control, nonlinear system modeling, compression of a fuzzy rule base, expansion of a sparse fuzzy rule base, fuzzy knowledge discovery, time series prediction, fuzzy games and pattern recognition. This effective soft computing system is able to perform both linguistic-word-level fuzzy reasoning and numerical-data-level information processing. The book also propose
Carrier Form: 1 online resource (xii,186pages) : illustrations.
Bibliography: Includes bibliographical references (pages 173-181) and index.
ISBN: 9789812797674
Index Number: QA76
CLC: TP183
Contents: 1. Introduction. 1.1. Fuzzy sets and data granularity. 1.2. Neural networks and knowledge discovery. 1.3. Genetic algorithms and adaptive optimization. 1.4. Soft computing systems and computational intelligence. 1.5. Main issues -- 2. Fuzzy compensation principles. 2.1. Fuzzy yin-yang compensation. 2.2. Compensation of fuzzy CNF and fuzzy DNF. 2.3. 2-variable-2-dimensional CNFs and DNFs. 2.4. 2-variable-m-dimensional CNFs and DNFs for m = 3,4. 2.5. Compensation of universal fuzzy CNF and fuzzy DNF. 2.6. Summary -- 3. Normal fuzzy reasoning methodology. 3.1. Primary fuzzy subsets. 3.2. The va

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