Studies in Fuzziness and Soft Computing

By: Yaochu Jin

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Hardcover | 18 November 2002

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Hardcover
292 Pages

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23.5 x 15.88 x 1.91

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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.

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Fuzzy Sets and Fuzzy Systems	p. 1
Evolutionary Algorithms	p. 49
Artificial Neural Networks	p. 73
Conventional Data-driven Fuzzy Systems Design	p. 93
Neural Network Based Fuzzy Systems Design	p. 111
Evolutionary Design of Fuzzy Systems	p. 143
Knowledge Discovery by Extracting Interpretable Fuzzy Rules	p. 173
Fuzzy Knowledge Incorporation into Neural Networks	p. 205
Fuzzy Preferences Incorporation into Multi-objective Optimization	p. 223
References	p. 268
Index	p. 269
Table of Contents provided by Blackwell. All Rights Reserved.

Studies in Fuzziness and Soft Computing

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