Fuzzy identification of nonlinear dynamic system based on selection of important input variables  

作　　者：LYU Jinfeng LIU Fucai REN Yaxue 

机构地区：[1]Engineering Research Center of the Ministry of Education for Intelligent Control System and Intelligent Equipment,Yanshan University,Qinhuangdao 066004,China [2]School of Mathematics and Information Science and Technology,Hebei Normal University of Science and Technology,Qinhuangdao 066004,China

出　　处：《Journal of Systems Engineering and Electronics》2022年第3期737-747,共11页系统工程与电子技术（英文版）

基　　金：This work was supported by the Natural Science Foundation of Hebei Province(F2019203505).

摘　　要：Input variables selection(IVS) is proved to be pivotal in nonlinear dynamic system modeling. In order to optimize the model of the nonlinear dynamic system, a fuzzy modeling method for determining the premise structure by selecting important inputs of the system is studied. Firstly, a simplified two stage fuzzy curves method is proposed, which is employed to sort all possible inputs by their relevance with outputs, select the important input variables of the system and identify the structure.Secondly, in order to reduce the complexity of the model, the standard fuzzy c-means clustering algorithm and the recursive least squares algorithm are used to identify the premise parameters and conclusion parameters, respectively. Then, the effectiveness of IVS is verified by two well-known issues. Finally, the proposed identification method is applied to a realistic variable load pneumatic system. The simulation experiments indi cate that the IVS method in this paper has a positive influence on the approximation performance of the Takagi-Sugeno(T-S) fuzzy modeling.

关 键 词：Takagi-Sugeno(T-S)fuzzy modeling input variable selection(IVS) fuzzy identification fuzzy c-means clustering algorithm 

分 类 号：N93[自然科学总论]