Fuzzy identification of nonlinear system considering the selection of important input variables

被引：0
|
作者：
Liu F.-C. [1 ]
Lü J.-F. [1 ,2 ]
Ren Y.-X. [1 ]
机构：
[1] Engineering Research Center of the Ministry of Education for Intelligent Control System and Intelligent Equipment, Yanshan University, Qinhuangdao
[2] Hebei Normal University of Science and Technology, Qinhuangdao
来源：
Kongzhi Lilun Yu Yingyong/Control Theory and Applications | 2021年 / 38卷 / 09期
关键词：
Fuzzy C-means (FCM) algorithm; Fuzzy systems; Gaussian function; Input variable selection; Pneumatic loading system; Takagi-Sugeno (T-S) model;
D O I：
10.7641/CTA.2021.00686
中图分类号：
学科分类号：
摘要：
Aiming at the fuzzy modeling of complex nonlinear dynamic systems with hundreds of possible inputs, this paper proposes a new fuzzy identification method considering the selection of important input variables. First, the two stage fuzzy curves method (TSFC) is used to give the weight of the correlation between each input variable and the output from a large number of selectable input variables, and the important input variable is quickly selected according to the input variable index. Then fuzzy C-means clustering (FCM) and Gaussian membership functions are used to determine the premise parameters of the fuzzy model, and recursive least squares (RLS) are used to identify the conclusion parameters of the fuzzy model. Finally, the effectiveness of the method is verified by fuzzy modeling of two international standard examples of Mackey-Glass Chaotic System and Box-Jenkins system. In order to verify the practicability of this method, this method is applied to the fuzzy modeling of an actual variable load pneumatic loading system. © 2021, Editorial Department of Control Theory & Applications South China University of Technology. All right reserved.
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页码：1381 / 1392
页数：11
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