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http://dx.doi.org/10.5391/JKIIS.2011.21.6.686

Design of IG-based Fuzzy Models Using Improved Space Search Algorithm  

Oh, Sung-Kwun (수원대학교 전기공학과)
Kim, Hyun-Ki (수원대학교 전기공학과)
Publication Information
Journal of the Korean Institute of Intelligent Systems / v.21, no.6, 2011 , pp. 686-691 More about this Journal
Abstract
This study is concerned with the identification of fuzzy models. To address the optimization of fuzzy model, we proposed an improved space search evolutionary algorithm (ISSA) which is realized with the combination of space search algorithm and Gaussian mutation. The proposed ISSA is exploited here as the optimization vehicle for the design of fuzzy models. Considering the design of fuzzy models, we developed a hybrid identification method using information granulation and the ISSA. Information granules are treated as collections of objects (e.g. data) brought together by the criteria of proximity, similarity, or functionality. The overall hybrid identification comes in the form of two optimization mechanisms: structure identification and parameter identification. The structure identification is supported by the ISSA and C-Means while the parameter estimation is realized via the ISSA and weighted least square error method. A suite of comparative studies show that the proposed model leads to better performance in comparison with some existing models.
Keywords
Improved space search algorithm (ISSA); Fuzzy inference systems (FIS); Information Granulation (IG);
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