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A structure identification method of submodels for hierarchical fuzzy modeling using the multiple objective genetic algorithm
Author(s) -
Tachibana Kanta,
Furuhashi Takeshi
Publication year - 2002
Publication title -
international journal of intelligent systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.291
H-Index - 87
eISSN - 1098-111X
pISSN - 0884-8173
DOI - 10.1002/int.10034
Subject(s) - fuzzy logic , identification (biology) , computer science , defuzzification , genetic algorithm , data mining , fuzzy set operations , algorithm , adaptive neuro fuzzy inference system , division (mathematics) , selection (genetic algorithm) , fuzzy classification , neuro fuzzy , hierarchical database model , mathematics , fuzzy number , fuzzy control system , artificial intelligence , fuzzy set , machine learning , arithmetic , botany , biology
Fuzzy models describe nonlinear input‐output relationships with linguistic fuzzy rules. A hierarchicalfuzzy 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. Thisarticle presents a hierarchical fuzzy modeling method with an uneven division method of input space of eachsubmodel. For selecting input variables of submodels, the multiple objective genetic algorithm (MOGA) isutilized. MOGA finds multiple models with different input variables and different numbers of fuzzy rules ascompromising solutions. A human designer can choose desirable ones from these candidates. The proposed method isapplied to acquisition of fuzzy rules from cyclists' pedaling data. In spite of a small number of data, theobtained model was able to give detailed suggestions to each cyclist. © 2002 Wiley Periodicals, Inc.