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 针对模糊聚类算法不适应复杂环境的问题,提出了一种新的动态进化聚类算法,克服了传统模糊聚类建模算法须事先确定规则数的缺陷.通过改进的遗传策略来优化染色体长度,实现对聚类个数进行全局寻优;利用fcm算法加快聚类中心参数的收敛;并引入免疫系统的记忆功能和疫苗接种机理,使算法能快速稳定地收敛到最优解.利用这种高效的动态聚类算法辨识模糊模型,可同时得到合适的模糊规则数和准确的前提参数,将其应用于控制过程可获得高精度的非线性模糊模型
