Proceedings of the KIEE Conference (대한전기학회:학술대회논문집)
- 2004.11c
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- Pages.340-342
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- 2004
Optimal Design of Fuzzy Relation-based Fuzzy Inference Systems Based on Evolutionary Information Granulation
진화론적 정보 입자에 기반한 퍼지 관계 기반 퍼지 추론 시스템의 최적 설계
- Published : 2004.11.12
Abstract
In this paper, we introduce a new category of fuzzy inference systems baled on information granulation to carry out the model identification of complex and nonlinear systems. Informal speaking, information granules are viewed as linked collections of objects(data, in particular) drawn together by the criteria of proximity, similarity, or functionality. Granulation of information with the aid of Hard C-Means(HCM) clustering algorithm help determine the initial parameters of fuzzy model such as the initial apexes of the membership functions and the initial values of polyminial functions being used in the premise and consequence part of the fuzzy rules. And the initial parameters are tuned effectively with the aid of the genetic algorithms(GAs) and the least square method. The proposed model is contrasted with the performance of the conventional fuzzy models in the literature.
Keywords
- Information Granulation;
- HCM Clustering;
- Fuzzy Relation;
- Fuzzy Inference Systems;
- Genetic Algorithms