Mathematical Problems in Engineering
Volume 2012 (2012), Article ID 893807, 12 pages
http://dx.doi.org/10.1155/2012/893807
Research Article

A Novel Identification Method for Generalized T-S Fuzzy Systems

Ling Huang,1 Kai Wang,1 Peng Shi,1,2,3 and Hamid Reza Karimi4

1School of Automation, Harbin University of Science and Technology, Harbin 150080, China
2Department of Computing and Mathematical Sciences, University of Glamorgan, Pontypridd CF37 1DL, UK
3School of Engineering and Science, Victoria University, Melbourne, VIC 8001, Australia
4Department of Engineering, Faculty of Engineering and Science, University of Agder, 4898 Grimstad, Norway

Received 1 August 2012; Accepted 17 October 2012

Academic Editor: Mohammed Chadli

Copyright © 2012 Ling Huang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

In order to approximate any nonlinear system, not just affine nonlinear systems, generalized T-S fuzzy systems, where the control variables and the state variables, are all premise variables are introduced in the paper. Firstly, fuzzy spaces and rules were determined by using ant colony algorithm. Secondly, the state-space model parameters are identified by using genetic algorithm. The simulation results show the effectiveness of the proposed algorithm.