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Advanced Fuzzy Systems Design and Applications

  • Book
  • © 2003

Overview

Authors:
  1. Yaochu Jin

    1. Future Technology Research, Honda R&D Europe GmbH, Offenbach/Main, Germany

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Part of the book series: Studies in Fuzziness and Soft Computing (STUDFUZZ, volume 112)

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Table of contents (9 chapters)

  1. Front Matter

    Pages I-X
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  2. Fuzzy Sets and Fuzzy Systems

    • Yaochu Jin
    Pages 1-47

  3. Evolutionary Algorithms

    • Yaochu Jin
    Pages 49-71

  4. Artificial Neural Networks

    • Yaochu Jin
    Pages 73-91

  5. Conventional Data-driven Fuzzy Systems Design

    • Yaochu Jin
    Pages 93-110

  6. Neural Network Based Fuzzy Systems Design

    • Yaochu Jin
    Pages 111-141

  7. Evolutionary Design of Fuzzy Systems

    • Yaochu Jin
    Pages 143-171

  8. Knowledge Discovery by Extracting Interpretable Fuzzy Rules

    • Yaochu Jin
    Pages 173-204

  9. Fuzzy Knowledge Incorporation into Neural Networks

    • Yaochu Jin
    Pages 205-221

  10. Fuzzy Preferences Incorporation into Multi-objective Optimization

    • Yaochu Jin
    Pages 223-253

  11. Back Matter

    Pages 255-272
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Keywords

About this book

Fuzzy rule systems have found a wide range of applications in many fields of science and technology. Traditionally, fuzzy rules are generated from human expert knowledge or human heuristics for relatively simple systems. In the last few years, data-driven fuzzy rule generation has been very active. Compared to heuristic fuzzy rules, fuzzy rules generated from data are able to extract more profound knowledge for more complex systems. This book presents a number of approaches to the generation of fuzzy rules from data, ranging from the direct fuzzy inference based to neural net­ works and evolutionary algorithms based fuzzy rule generation. Besides the approximation accuracy, special attention has been paid to the interpretabil­ ity of the extracted fuzzy rules. In other words, the fuzzy rules generated from data are supposed to be as comprehensible to human beings as those generated from human heuristics. To this end, many aspects of interpretabil­ ity of fuzzy systems have been discussed, which must be taken into account in the data-driven fuzzy rule generation. In this way, fuzzy rules generated from data are intelligible to human users and therefore, knowledge about unknown systems can be extracted.

Authors and Affiliations

  • Future Technology Research, Honda R&D Europe GmbH, Offenbach/Main, Germany

    Yaochu Jin

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