Paper Info
Title
A Structure Identification Method Of Submodels For Hierarchical Fuzzy Modeling Using The Multiple Objective Genetic Algorithm
Abstract
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.
Year
DOI
Venue
2002
10.1002/int.10034
INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS
Keywords
Field
DocType
genetic algorithm
Data mining,Neuro-fuzzy,Defuzzification,Fuzzy classification,Fuzzy set operations,Fuzzy logic,Adaptive neuro fuzzy inference system,Fuzzy associative matrix,Fuzzy number,Mathematics
Journal
Volume
Issue
ISSN
17
5
0884-8173
Citations 
PageRank 
References 
4
0.48
9
Authors
2
Name
Order
Citations
PageRank
Kanta Tachibana1124.81
Takeshi Furuhashi253786.15