Overview

This book presents a variety of recently developed methods for generating fuzzy rules from data with the help of neural networks and evolutionary algorithms. Special efforts have been put on dealing with knowledge incorporation into neural and evolutionary systems and knowledge extraction from data with the help of fuzzy logic. On the one hand, knowledge that is understandable to human beings can be extracted from data using evolutionary and learning methods by maintaining the interpretability of the generated fuzzy rules. On the other hand, a priori knowledge like expert knowledge and human preferences can be incorporated into evolution and learning, taking advantage of the knowledge representation capability of fuzzy rule systems and fuzzy preference models. Several engineering application examples in the fields of intelligent vehicle systems, process modeling and control and robotics are presented.

Full Product Details

Author:   Yaochu Jin
Publisher:   Springer-Verlag Berlin and Heidelberg GmbH & Co. KG
Imprint:   Physica-Verlag GmbH & Co
Edition:   2003 ed.
Volume:   112
Dimensions:   Width: 15.50cm , Height: 1.70cm , Length: 23.30cm
Weight:   1.290kg
ISBN:  

9783790815375

ISBN 10:   3790815373
Pages:   272
Publication Date:   18 November 2002
Audience:   College/higher education ,  Professional and scholarly ,  Undergraduate ,  Postgraduate, Research & Scholarly
Format:   Hardback
Publisher's Status:   Active
Availability:   In Print  
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Table of Contents

1. Fuzzy Sets and Fuzzy Systems.- 1.1 Basics of Fuzzy Sets.- 1.2 Fuzzy Rule Systems.- 1.3 Interpretability of Fuzzy Rule System.- 1.4 Knowledge Processing with Fuzzy Logic.- 2. Evolutionary Algorithms.- 2.1 Introduction.- 2.2 Generic Evolutionary Algorithms.- 2.3 Adaptation and Self-Adaptation in Evolutionary Algorithms.- 2.4 Constraints Handling.- 2.5 Multi-objective Evolution.- 2.6 Evolution with Uncertain Fitness Functions.- 2.7 Parallel Implementations.- 2.8 Summary.- 3. Artificial Neural Networks.- 3.1 Introduction.- 3.2 Feedforward Neural Network Models.- 3.3 Learning Algorithms.- 3.4 Improvement of Generalization.- 3.5 Rule Extraction from Neural Networks.- 3.6 Interaction between Evolution and Learning.- 3.7 Summary.- 4. Conventional Data-driven Fuzzy Systems Design.- 4.1 Introduction.- 4.2 Fuzzy Inference Based Method.- 4.3 Wang-Mendel’s Method.- 4.4 A Direct Method.- 4.5 An Adaptive Fuzzy Optimal Controller.- 4.6 Summary.- 5.Neural Network Based Fuzzy Systems Design.- 5.1 Neurofuzzy Systems.- 5.2 The Pi-sigma Neurofuzzy Model.- 5.3 Modeling and Control Using the Neurofuzzy System.- 5.4 Neurofuzzy Control of Nonlinear Systems.- 5.5 Summary.- 6. Evolutionary Design of Fuzzy Systems.- 6.1 Introduction.- 6.2 Evolutionary Design of Flexible Structured Fuzzy Controller..- 6.3 Evolutionary Optimization of Fuzzy Rules.- 6.4 Fuzzy Systems Design for High-Dimensional Systems.- 6.5 Summary.- 7. Knowledge Discovery by Extracting Interpretable Fuzzy Rules.- 7.1 Introduction.- 7.2 Evolutionary Interpretable Fuzzy Rule Generation.- 7.3 Interactive Co-evolution for Fuzzy Rule Extraction.- 7.4 Fuzzy Rule Extraction from RBF Networks.- 7.5 Summary.- 8. Fuzzy Knowledge Incorporation into Neural Networks.- 8.1 Data and A Priori Knowledge.- 8.2 Knowledge Incorporation in NeuralNetworks for Control.- 8.3 Fuzzy Knowledge Incorporation By Regularization.- 8.4 Fuzzy Knowledge as A Related Task in Learning.- 8.5 Simulation Studies.- 8.6 Summary.- 9. Fuzzy Preferences Incorporation into Multi-objective Optimization.- 9.1 Multi-objective Optimization and Preferences Handling.- 9.2 Evolutionary Dynamic Weighted Aggregation.- 9.3 Fuzzy Preferences Incorporation in MOO.- 9.4 Summary.- References.

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