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Set-membership identification of T-S fuzzy models using support vector regression

He, Liqing and Sun, Xianfang 2009. Set-membership identification of T-S fuzzy models using support vector regression. Presented at: 2009 9th International Conference on Electronic Measurement and Instruments, Beijing, China, 16-19 August 2009. 9th International Conference on Electronic Measurement & Instruments, 2009, Beijing, China, 16-19 Aug. 2009. Los Alamitos, CA: IEEE, pp. 59-63. 10.1109/ICEMI.2009.5274796

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Abstract

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.

Item Type: Conference or Workshop Item (Paper)
Date Type: Publication
Status: Published
Schools: Computer Science & Informatics
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Uncontrolled Keywords: Nonlinear system , Set-membership identification , Support vector regression (SVR) , T-S model , Unknown-but-bounded (UBB) noise
Publisher: IEEE
ISBN: 9781424438631
Last Modified: 04 Jun 2017 02:56
URI: http://orca.cf.ac.uk/id/eprint/14244

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