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

He, Liqing and Sun, Xianfang ORCID: https://orcid.org/0000-0002-6114-0766 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

Full text not available from this repository.

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:	18 Oct 2022 13:32
URI:	https://orca.cardiff.ac.uk/id/eprint/14244

Citation Data

Cited 2 times in Scopus. View in Scopus. Powered By Scopus® Data

Actions (repository staff only)

Edit Item