A NOVEL DYNAMIC CLUSTERING ALGORITHM AND ITS APPLICATION IN FUZZY MODELING FOR THERMAL PROCESSES
A novel dynamic evolutionary clustering algorithm (DECA) is proposed in this paper to overcome the shortcomings of fuzzy modeling method based on general clustering algorithms that fuzzy rule number should be determined beforehand. DECA searches for the optimal cluster number by using the improved genetic techniques to optimize string lengths of chromosomes; at the same time, the convergence of clustering center parameters is expedited with the help of Fuzzy C-Means (FCM) algorithm. Moreover, by introducing memory function and vaccine inoculation mechanism of immune system, at the same time, DECA can converge to the optimal solution rapidly and stably. The proper fuzzy rule number and exact premise parameters are obtained simultaneously when using this efficient DECA to identify fuzzy models. The effectiveness of the proposed fuzzy modeling method based on DECA is demonstrated by simulation examples, and the accurate non-linear fuzzy models can be obtained when the method is applied to the thermal processes.
Dynamic clustering Fuzzy model Immune mechanism Genetic algorithm Thermal processes
WEI-JIN JIANG
School of computer, Hunan University of technology, Zhuzhou 412008, China
国际会议
2006 International Conference on Machine Learning and Cybernetics(IEEE第五届机器学习与控制论坛)
大连
英文
1221-1226
2006-08-13（万方平台首次上网日期，不代表论文的发表时间）