摘要
文中提出的新型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。它通过改进的遗传策略来优化染色体长度,以实现对聚类个数进行全局寻优;同时,利用FCM算法加快了聚类中心参数的收敛;此外,通过引入免疫系统的记忆功能和疫苗接种机理,新算法得以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,可以同时得到合适的模糊规则数和准确的前提参数。仿真实例验证了文中动态模糊聚类建模算法的有效性,将其应用于热工过程可获得高精度的非线性模糊模型。
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 cluster 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, 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 nonlinear fuzzy models can be obtained when the method is applied to the thermal processes.
出处
《中国电机工程学报》
EI
CSCD
北大核心
2005年第7期34-40,共7页
Proceedings of the CSEE
基金
教育部高等学校博士学科点专项科研基金(20020286001)
