Nonlinear System Identification Based on Adaptive Competitive Clustering and OLS
In this paper, A new identification method for nonlinear system model from input-output data is presented. In accordance with the problem that sensitivity to initialization and noise, and some relative parameters must be determined beforehand during the fuzzy clustering process in the usual fuzzy cluster algorithm, and the existing competitive clustering algorithm have poor convergence properties, and make convergence to a local minimum more likely. A type of adaptive competitive cluster algorithm for structure identification is presented. At the same time, orthogonal least squares (OLS) method algorithm is used to remove redundant fuzzy rules and identify model parameters during the clustering process. Through simulation research, the effectiveness of the method is proved.
Fuzzy Modeling Takagi-Sugeno Model Adaptive Competitive Clustering OLS
Hao wan-jun Qiao yan-hui Qiang wen-yi
College of Electrical and Information Engineering, Beihua University, Jilin 132021, China School of College of Electrical and Information Engineering, Beihua University, Jilin 132021, China School of Astronautics , Harbin Institute of Technology, Harbin 150001, China
国际会议
2009年中国控制与决策会议(2009 Chinese Control and Decision Conference)
广西桂林
英文
1178-1183
2009-06-17（万方平台首次上网日期，不代表论文的发表时间）