About the use of fuzzy clustering techniques for fuzzy model identification
Fuzzy Sets and Systems
Performance Evaluation of Some Clustering Algorithms and Validity Indices
IEEE Transactions on Pattern Analysis and Machine Intelligence
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A novel dynamic evolutionary clustering algorithm 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. This algorithm 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 algorithm. Moreover, by introducing memory function and vaccine inoculation mechanism of immune system, at the same time, dynamic evolutionary clustering algorithm 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 dynamic evolutionary clustering algorithm to identify fuzzy models. The effectiveness of the proposed fuzzy modeling method based on dynamic evolutionary clustering algorithm is demonstrated by simulation examples, and the accurate non-linear fuzzy models can be obtained when the method is applied to the thermal processes.