Application of Evolving Takagi-Sugeno Fuzzy Model to Nonlinear System Identification

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Show simple item record Du, Haiping en_US Zhang, Nong en_US
dc.contributor.editor en_US 2010-05-28T09:47:22Z 2010-05-28T09:47:22Z 2008 en_US
dc.identifier 2006010350 en_US
dc.identifier.citation Du Haiping and Zhang Nong 2008, 'Application of Evolving Takagi-Sugeno Fuzzy Model to Nonlinear System Identification', Elsevier, vol. 8, no. 1, pp. 676-686. en_US
dc.identifier.issn 1568-4946 en_US
dc.identifier.other C1 en_US
dc.description.abstract In this paper, a new encoding scheme is presented for learning the Takagi-Sugeno (T-S) fuzzy model from data by genetic algorithms (GAs). In the proposed encoding scheme, the rule structure ( selection of rules and number of rules), the input structure ( selection of inputs and number of inputs), and the antecedent membership function (MF) parameters of the T-S fuzzy model are all represented in one chromosome and evolved together such that the optimisation of rule structure, input structure, and MF parameters can be achieved simultaneously. The performance of the developed evolving T-S fuzzy model is first validated by studying the benchmark Box-Jenkins nonlinear system identification problem and nonlinear plant modelling problem, and comparing the obtained results with other existing results. Then, it is applied to approximate the forward and inverse dynamic behaviours of a magneto-rheological (MR) damper of which identification problem is significantly difficult due to its inherently hysteretic and highly nonlinear dynamics. It is shown by the validation applications that the developed evolving T-S fuzzy model can identify the nonlinear system satisfactorily with acceptable number of rules and appropriate inputs. (C) 2007 Elsevier B.V. All rights reserved. en_US
dc.language en_US
dc.publisher Elsevier en_US
dc.relation.isbasedon en_US
dc.relation.isbasedon en_US
dc.title Application of Evolving Takagi-Sugeno Fuzzy Model to Nonlinear System Identification en_US
dc.parent Applied Soft Computing en_US
dc.journal.volume 8 en_US
dc.journal.number 1 en_US
dc.publocation The Netherlands en_US
dc.identifier.startpage 676 en_US
dc.identifier.endpage 686 en_US FEIT.School of Elec, Mech and Mechatronic Systems en_US
dc.conference Verified OK en_US
dc.for 080600 en_US
dc.personcode 950854 en_US
dc.personcode 123171 en_US
dc.percentage 34 en_US Information Systems en_US
dc.classification.type FOR-08 en_US
dc.edition en_US
dc.custom en_US en_US
dc.location.activity en_US
dc.description.keywords takagi-sugeno fuzzy model; genetic algorithms; encoding; nonlinear system identification en_US
dc.staffid en_US
dc.staffid 950854 en_US

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