Robust fuzzy T-S modeling method based on minimizing mean and variance of modeling error

被引：1
|
作者：
Sui H. [1 ]
Qin G.-F. [1 ]
Cui X.-B. [1 ]
Lu X.-J. [1 ]
机构：
[1] State Key Laboratory of High Performance Complex Manufacturing, School of Mechanical and Electrical Engineering, Central South University, Changsha
来源：
Zhejiang Daxue Xuebao (Gongxue Ban)/Journal of Zhejiang University (Engineering Science) | 2019年 / 53卷 / 02期
关键词：
Generalization; Nonlinear system; Robustness; Structural risk; T-S fuzzy modeling method;
D O I：
10.3785/j.issn.1008-973X.2019.02.022
中图分类号：
学科分类号：
摘要：
Traditional T-S fuzzy modeling method has been widely and successfully used to model nonlinear systems with noise. However, most of the existing parameters identification methods for T-S model do not consider structural risk item, which would lead to poor generalization. Although traditional T-S fuzzy modeling method could achieve good recognition effect under Gaussian noise, the identification effect under non-Gaussian noise or outliers is poor, because the mean and variance items of error are not comprehensively considered. The robust fuzzy T-S modeling method was proposed to overcome the weakness of the traditional modeling method. The new modeling method constructed a new objective function to identify the parameters. The new objective function not only considered structural risk, but also minimized the mean and variance of error, which would lead to better generalization and robustness. Simulation and experiment results showed that the new modeling method can effectively model the nonlinear system under the noise interference, and the modeling effect was superior to that of the traditional T-S fuzzy modeling method. © 2019, Zhejiang University Press. All right reserved.
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页码：382 / 387and398
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