A Novel Identification Method for Generalized T-S Fuzzy Systems

A Novel Identification Method for Generalized T-S Fuzzy Systems

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. S...

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Main Authors: Ling Huang, Kai Wang, Peng Shi, Hamid Reza Karimi
Format: Article
Language:English
Published: Hindawi Limited 2012-01-01
Series:Mathematical Problems in Engineering
Online Access:http://dx.doi.org/10.1155/2012/893807
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spelling doaj-07a0d4da00df4a258f5e72bb0a2467e12020-11-24T23:38:20ZengHindawi LimitedMathematical Problems in Engineering1024-123X1563-51472012-01-01201210.1155/2012/893807893807A Novel Identification Method for Generalized T-S Fuzzy SystemsLing Huang0Kai Wang1Peng Shi2Hamid Reza Karimi3School of Automation, Harbin University of Science and Technology, Harbin 150080, ChinaSchool of Automation, Harbin University of Science and Technology, Harbin 150080, ChinaSchool of Automation, Harbin University of Science and Technology, Harbin 150080, ChinaDepartment of Engineering, Faculty of Engineering and Science, University of Agder, 4898 Grimstad, NorwayIn 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.http://dx.doi.org/10.1155/2012/893807
collection DOAJ
language English
format Article
sources DOAJ
author Ling Huang
Kai Wang
Peng Shi
Hamid Reza Karimi
spellingShingle Ling Huang
Kai Wang
Peng Shi
Hamid Reza Karimi
A Novel Identification Method for Generalized T-S Fuzzy Systems
Mathematical Problems in Engineering
author_facet Ling Huang
Kai Wang
Peng Shi
Hamid Reza Karimi
author_sort Ling Huang
title A Novel Identification Method for Generalized T-S Fuzzy Systems
title_short A Novel Identification Method for Generalized T-S Fuzzy Systems
title_full A Novel Identification Method for Generalized T-S Fuzzy Systems
title_fullStr A Novel Identification Method for Generalized T-S Fuzzy Systems
title_full_unstemmed A Novel Identification Method for Generalized T-S Fuzzy Systems
title_sort novel identification method for generalized t-s fuzzy systems
publisher Hindawi Limited
series Mathematical Problems in Engineering
issn 1024-123X
1563-5147
publishDate 2012-01-01
description 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.
url http://dx.doi.org/10.1155/2012/893807
work_keys_str_mv AT linghuang anovelidentificationmethodforgeneralizedtsfuzzysystems
AT kaiwang anovelidentificationmethodforgeneralizedtsfuzzysystems
AT pengshi anovelidentificationmethodforgeneralizedtsfuzzysystems
AT hamidrezakarimi anovelidentificationmethodforgeneralizedtsfuzzysystems
AT linghuang novelidentificationmethodforgeneralizedtsfuzzysystems
AT kaiwang novelidentificationmethodforgeneralizedtsfuzzysystems
AT pengshi novelidentificationmethodforgeneralizedtsfuzzysystems
AT hamidrezakarimi novelidentificationmethodforgeneralizedtsfuzzysystems
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