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http://dx.doi.org/10.5391/JKIIS.2012.22.4.399

Design of Fuzzy Models with the Aid of an Improved Differential Evolution  

Kim, Hyun-Ki (수원대학교 전기공학과)
Oh, Sung-Kwun (수원대학교 전기공학과)
Publication Information
Journal of the Korean Institute of Intelligent Systems / v.22, no.4, 2012 , pp. 399-404 More about this Journal
Abstract
Evolutionary algorithms such as genetic algorithm (GA) have been proven their effectiveness when applying to the design of fuzzy models. However, it tends to suffer from computationally expensWive due to the slow convergence speed. In this study, we propose an approach to develop fuzzy models by means of an improved differential evolution (IDE) to overcome this limitation. The improved differential evolution (IDE) is realized by means of an orthogonal approach and differential evolution. With the invoking orthogonal method, the IDE can search the solution space more efficiently. In the design of fuzzy models, we concern two mechanisms, namely structure identification and parameter estimation. The structure identification is supported by the IDE and C-Means while the parameter estimation is realized via IDE and a standard least square error method. Experimental studies demonstrate that the proposed model leads to improved performance. The proposed model is also contrasted with the quality of some fuzzy models already reported in the literature.
Keywords
Improved Differential Evolution (IDE); Fuzzy Inference System (FIS); Information Granulation (IG); C-Means clustering; Least Square Method (LSM);
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Times Cited By KSCI : 3  (Citation Analysis)
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