摘要
针对引力搜索算法在求解复杂问题时搜索精度较低、易出现早熟收敛的缺点。提出一个新颖的智能算法-基于基因突变的引力搜索算法来辨识T-S模型的参数,同时提出一种改进的聚类算法辨识T-S模型的结构,实验结果表明,改进算法辨识出的T-S模型结构紧凑、精度更高,且泛化能力强。
As the gravitational search algorithm plays a negative influence on the search accuracy of the complex issues, especially the poor search quality of standard Gravitational Search Algorithm（GSA） in the high dimensional function optimization, it is easy to get into premature convergence in the optimization process. An improved gravita- tional search algorithm based on genetic mutations（gmGSA） is proposed to identify the parameter of T-S model. An improved fuzzy c-means（FCM） based on simulated annealing（SA） and genetic algorithm（GA）, denote as SAGA- FCM, is also proposed to identify the structure of T-S model. The simulation results show the proposed methods can effectively obtain compact and accurate fuzzy models with excellent capability of generalization.
出处
《电子科技》
2015年第11期16-20,共5页
Electronic Science and Technology
基金
沪江基金资助项目(A14001
B1402
D1402)
关键词
T-S模型辨识
基因突变
FCM聚类
引力搜索算法
identification of T-S model
genetic mutations
FCM clustering
gravitational search algorithm
