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A novel identification method for generalized T-S fuzzy systems

Peng ShiPeng ShiPeng ShiKai WangHamid Reza KarimiLing Huang

subject

VDP::Mathematics and natural science: 400::Mathematics: 410::Applied mathematics: 413State variableMathematical optimizationArticle SubjectGeneral MathematicsAnt colony optimization algorithmsPopulation-based incremental learninglcsh:MathematicsVDP::Technology: 500General EngineeringFuzzy control systemlcsh:QA1-939Fuzzy logicNonlinear systemlcsh:TA1-2040Fuzzy set operationslcsh:Engineering (General). Civil engineering (General)AlgorithmMathematicsFSA-Red Algorithm

description

Published version of an article from the journal: Mathematical Problems in Engineering. Also available from the publisher:http://dx.doi.org/10.1155/2012/893807 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

yearjournalcountryeditionlanguage
2012-01-01
http://hdl.handle.net/11250/136928