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http://dx.doi.org/10.5391/JKIIS.2002.12.3.231

Fuzzy Modeling and Fuzzy Rule Generation in Global Approximate Response Surfaces  

Lee, Jong-Soo (연세대학교 기계공학부)
Hwang, Jeong-Su (연세대학교 대학원 기계공학과)
Publication Information
Journal of the Korean Institute of Intelligent Systems / v.12, no.3, 2002 , pp. 231-238 More about this Journal
Abstract
As a modeling method where the merits of fuzzy inference system and evolutionary computation are put together, evolutionary fuzzy modeling performs global approximate optimization. The paper proposes fuzzy clustering as fuzzy rule generation process which is one of the most important steps in evolutionary fuzzy modeling. With application of fuzzy clustering into the experiment or simulation results, fuzzy rules which properly describe non-linear and complex design problem can be obtained. The efficiency of evolutionary fuzzy modeling can be improved utilizing the membership degrees of data to clusters from the results of fuzzy clustering. To ensure the validity of the proposed method, the real design problem of an automotive inner trim is applied and the global approximation is achieved. Evolutionary fuzzy modeling is performed for several cases which differ in the number of clusters and the criterion of rule selection and their results are compared to prove that the proposed method can provide proper fuzzy rules for a given system and reduce computation time while maintaining the errors of modeling as a satisfactory level.
Keywords
전역근사최적화;진화퍼지모델링;퍼지클러스터링;퍼지규칙;
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