Set-membership Identification of T-S Fuzzy Models Using Support Vector Regression

摘要：

In this paper,the problem of identifying nonlinear systems with unknown-but-bounded (UBB) noise is investigated. The fuzzy inference theory and support vector regression (SVR) learning mechanism are used to construct a T-S model for the nonlinear system based on input and output data with UBB measurement noise. After the structure of a T-S model is determined using SVR,all the feasible parameters in its consequent part are found by the optimal bounding ellipsoid (OBE) algorithm and then a class of feasible nonlinear models are found which are consistent with the given noise bound series and input-output data set. The simulation results illustrate that the proposed method is effective.

关键词： Nonlinear system Unknown-but-bounded (UBB) noise T-S model Set-membership identification Support vector regression (SVR).

作者: Liqing He Xianfang Sun

作者单位: School of Automation Science and Electrical Engineering,Beihang University Xuanyuan Road No.37,Haidian District,Beijing 100191,China

会议类型: 国际会议

会议名称: 2009 9th International Conference on Electronic Measurement & Instruments(第九届电子测量与仪器国际会议 ICEMI2009)

会议地点: 北京

会议语种:英文

页码: 59-63

在线出版日期: 2009-08-16（万方平台首次上网日期，不代表论文的发表时间）