| Title |
Optimization of Fuzzy Systems by Means of GA and Weighting Factor |
| Authors |
박병준(Park, Byoung-Jun) ; 오성권(Oh, Sung-Kwun) ; 안태천(Ahn, Tae-Chon) ; 김현기(Kim, Hyun-Ki) |
| Keywords |
퍼지추론 시스템 ; 동정 ; 유전자 알고리즘 ; 하중값 ; 클러스터링 방법 Fuzzy inference system ; Identification ; Genetic algorithms ; Weighting factor ; Clustering Method |
| Abstract |
In this paper, the optimization of fuzzy inference systems is proposed for fuzzy model of nonlinear systems. A fuzzy model needs to be identified and optimized by means of the definite and systematic methods, because a fuzzy model is primarily acquired by expert's experience. The proposed rule-based fuzzy model implements system structure and parameter identification using the HCM(Hard C-mean) clustering method, genetic algorithms and fuzzy inference method. Two types of inference methods of a fuzzy model are the simplified inference and linear inference. in this paper, nonlinear systems are expressed using the identification of structure such as input variables and the division of fuzzy input subspaces, and the identification of parameters of a fuzzy model. To identify premise parameters of fuzzy model, the genetic algorithms is used and the standard least square method with the gaussian elimination method is utilized for the identification of optimum consequence parameters of fuzzy model. Also, the performance index with weighting factor is proposed to achieve a balance between the performance results of fuzzy model produced for the training and testing data set, and it leads to enhance approximation and predictive performance of fuzzy system. Time series data for gas furnace and sewage treatment process are used to evaluate the performance of the proposed model. |