Advanced Fuzzy Systems Design and Applications: 112 (Studies in Fuzziness and Soft Computing, 112)

Jin, Yaochu

ISBN 10: 3790815373 / ISBN 13: 9783790815375

Published by Physica, 2002

About this Item

Using neural networks and evolutionary algorithms, this book presents a variety of recently developed methods for generating fuzzy rules from data. It considers knowledge incorporation into neural and evolutionary systems and knowledge extraction from data with fuzzy logic. Series: Studies in Fuzziness and Soft Computing. Num Pages: 272 pages, 228 black & white illustrations, 10 black & white tables, biography. BIC Classification: PBWX; UMB; UYQ. Category: (P) Professional & Vocational; (UP) Postgraduate, Research & Scholarly; (UU) Undergraduate. Dimension: 233 x 155 x 17. Weight in Grams: 586. . 2002. 2003rd Edition. hardcover. . . . . Books ship from the US and Ireland.

Seller Inventory # V9783790815375

Bibliographic Details

• Title: Advanced Fuzzy Systems Design and Applications: 112 (Studies in Fuzziness and Soft Computing, 112)
• Publisher: Physica
• Publication Date: 2002
• Language: English
• ISBN 10: 3790815373
• ISBN 13: 9783790815375
• Binding: Hardcover
• Condition: New

About this title

Synopsis

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.