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Design of IG-based Fuzzy Models Using Improved Space Search Algorithm
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  • Design of IG-based Fuzzy Models Using Improved Space Search Algorithm
  • Design of IG-based Fuzzy Models Using Improved Space Search Algorithm
저자명
오성권,김현기,Oh. Sung-Kwun,Kim. Hyun-Ki
간행물명
한국지능시스템학회 논문지
권/호정보
2011년|21권 6호|pp.686-691 (6 pages)
발행정보
한국지능시스템학회
파일정보
정기간행물|ENG|
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이 논문은 한국과학기술정보연구원과 논문 연계를 통해 무료로 제공되는 원문입니다.
서지반출

기타언어초록

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.