Overview

The main idea of this book is to present novel connectionist architectures of neuro-fuzzy systems, especially those based on the logical approach to fuzzy inference. In addition, hybrid learning methods are proposed to train the networks. The neuro-fuzzy architectures plus hybrid learning are considered as intelligent systems within the framework of computational and artificial intelligence. The book also provides an overview of fuzzy sets and systems, neural networks, learning algorithms (including genetic algorithms and clustering methods), as well as expert systems and perception-based systems which incorporates computing with words.

Full Product Details

Author:   Danuta Rutkowska
Publisher:   Springer-Verlag Berlin and Heidelberg GmbH & Co. KG
Imprint:   Physica-Verlag GmbH & Co
Edition:   Softcover reprint of hardcover 1st ed. 2002
Volume:   85
Dimensions:   Width: 15.50cm , Height: 1.60cm , Length: 23.50cm
Weight:   0.468kg
ISBN:  

9783790825008

ISBN 10:   379082500
Pages:   288
Publication Date:   21 October 2010
Audience:   Professional and scholarly ,  Professional & Vocational
Format:   Paperback
Publisher's Status:   Active
Availability:   In Print  
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Table of Contents

1 Introduction.- 2 Description of Fuzzy Inference Systems.- 2.1 Fuzzy Sets.- 2.2 Approximxate Reasoning.- 2.3 Fuzzy Systems.- 3 Neural Networks and Neuro-Fuzzy Systems.- 3.1 Neural Networks.- 3.2 Fuzzy Neural Networks.- 3.3 Fuzzy Inference Neural Networks.- 4 Neuro-Fuzzy Architectures Based on the Mamdani Approach.- 4.1 Basic Architectures.- 4.2 General Form of the Architectures.- 4.3 Systems with Inference Based on Bounded Product.- 4.4 Simplified Architectures.- 4.5 Architectures Based on Other Defuzzification Methods.- 4.6 Architectures of Systems with Non-Singleton Fuzzifier.- 5 Neuro-Fuzzy Architectures Based on the Logical Approach.- 5.1 Mathematical Descriptions of Implication-Based Systems.- 5.2 NOCFS Architectures.- 5.3 OCFS Architectures.- 5.4 Performance Analysis.- 5.5 Computer Simulations.- 6 Hybrid Learning Methods.- 6.1 Gradient Learning Algorithms.- 6.2 Genetic Algorithms.- 6.3 Clustering Algorithms.- 6.4 Hybrid Learning.- 6.5 Hybrid Learning Algorithms for Neuro-Fuzzy Systems.- 7 Intelligent Systems.- 7.1 Artificial and Computational Intelligence.- 7.2 Expert Systems.- 7.3 Intelligent Computational Systems.- 7.4 Perception-Based Intelligent Systems.- 8 Summary.- List of Figures.- List of Tables.- References.

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