MISC

2002年5月

A structure identification method of submodels for hierarchical fuzzy modeling using the multiple objective genetic algorithm

INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS
  • K Tachibana
  • ,
  • T Furuhashi

17
5
開始ページ
495
終了ページ
513
記述言語
英語
掲載種別
DOI
10.1002/int.10034
出版者・発行元
WILEY-BLACKWELL

Fuzzy models describe nonlinear input-output relationships with linguistic fuzzy rules. A hierarchical fuzzy modeling is promising for identification of fuzzy models of target systems that have many input variables. In the identification, (1) determination of a hierarchical structure of submodels, (2) selection of input variables of each submodel, (3) division of input and output space, (4) tuning of membership functions, and (5) determination of fuzzy inference method are carried out. This article presents a hierarchical fuzzy modeling method with an uneven division method of input space of each submodel. For selecting input variables of submodels, the multiple objective genetic algorithm (MOGA) is utilized. MOGA finds multiple models with different input variables and different numbers of fuzzy rules as compromising solutions. A human designer can choose desirable ones from these candidates. The proposed method is applied to acquisition of fuzzy rules from cyclists' pedaling data. In spite of a small number of data, the obtained model was able to give detailed suggestions to each cyclist. (C) 2002 Wiley Periodicals, Inc.

Web of Science ® 被引用回数 : 8

リンク情報
DOI
https://doi.org/10.1002/int.10034
Web of Science
https://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=JSTA_CEL&SrcApp=J_Gate_JST&DestLinkType=FullRecord&KeyUT=WOS:000175073500004&DestApp=WOS_CPL

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