TIIS (한국인터넷정보학회)
Current Result Document :
| 한글제목(Korean Title) |
Identification of Fuzzy Inference System Based on Information Granulation |
| 영문제목(English Title) |
Identification of Fuzzy Inference System Based on Information Granulation |
| 저자(Author) |
Wei Huang
Lixin Ding
Sung-Kwun Oh
Chang-Won Jeo
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| 원문수록처(Citation) |
VOL 04 NO. 04 PP. 0575 ~ 0594 (2010. 08) |
한글내용
(Korean Abstract) |
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영문내용
(English Abstract) |
In this study, we propose a space search algorithm (SSA) and then introduce a hybrid optimization of fuzzy inference systems based on SSA and information granulation (IG). In comparison with “conventional” evolutionary algorithms (such as PSO), SSA leads no.t only to better search performance to find global optimization but is also more computationally effective when dealing with the optimization of the fuzzy models. In the hybrid optimization of fuzzy inference system, SSA is exploited to carry out the parametric optimization of the fuzzy model as well as to realize its structural optimization. IG realized with the aid of C-Means clustering helps determine the initial values of the apex parameters of the membership function of fuzzy model. The overall hybrid identification of fuzzy inference systems comes in the form of two optimization mechanisms: structure identification (such as the number of input variables to be used, a specific subset of input variables, the number of membership functions, and polyno.mial type) and parameter identification (viz. the apexes of membership function). The structure identification is developed by SSA and C-Means while the parameter estimation is realized via SSA and a standard least square method. The evaluation of the performance of the proposed model was carried out by using four representative numerical examples such as No.n-linear function, gas furnace, NO.x emission process data, and Mackey-Glass time series. A comparative study of SSA and PSO demonstrates that SSA leads to improved performance both in terms of the quality of the model and the computing time required. The proposed model is also contrasted with the quality of some “conventional” fuzzy models already encountered in the literature. |
| 키워드(Keyword) |
Space search algorithm (SSA)
particle swarm algorithm (PSO)
information granulation (IG)
fuzzy inference system (FIS)
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| 파일첨부 |
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