Application of evolving Takagi-Sugeno fuzzy model to nonlinear system identification

DSpace/Manakin Repository

Search OPUS


Advanced Search

Browse

My Account

Show simple item record

dc.contributor.author Du, H
dc.contributor.author Zhang, N
dc.date.accessioned 2010-05-28T09:47:22Z
dc.date.issued 2008-01
dc.identifier.citation Applied Soft Computing Journal, 2008, 8 (1), pp. 676 - 686
dc.identifier.issn 1568-4946
dc.identifier.other C1 en_US
dc.identifier.uri http://hdl.handle.net/10453/9089
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. © 2007 Elsevier B.V. All rights reserved.
dc.language eng
dc.relation.isbasedon 10.1016/j.asoc.2007.05.006
dc.title Application of evolving Takagi-Sugeno fuzzy model to nonlinear system identification
dc.type Journal Article
dc.parent Applied Soft Computing Journal
dc.journal.volume 1
dc.journal.volume 8
dc.journal.number 1 en_US
dc.publocation The Netherlands en_US
dc.identifier.startpage 676 en_US
dc.identifier.endpage 686 en_US
dc.cauo.name FEIT.School of Elec, Mech and Mechatronic Systems en_US
dc.conference Verified OK en_US
dc.for 0801 Artificial Intelligence and Image Processing
dc.for 0806 Information Systems
dc.for 0102 Applied Mathematics
dc.personcode 950854
dc.personcode 123171
dc.percentage 34 en_US
dc.classification.name Information Systems en_US
dc.classification.type FOR-08 en_US
dc.edition en_US
dc.custom en_US
dc.date.activity en_US
dc.location.activity en_US
dc.description.keywords Encoding
dc.description.keywords Genetic algorithms
dc.description.keywords Nonlinear system identification
dc.description.keywords Takagi-Sugeno fuzzy model
pubs.embargo.period Not known
pubs.organisational-group /University of Technology Sydney
pubs.organisational-group /University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group /University of Technology Sydney/Faculty of Engineering and Information Technology/School of Elec, Mech and Mechatronic Systems
utslib.copyright.status Closed Access
utslib.copyright.date 2015-04-15 12:17:09.805752+10
pubs.consider-herdc true
utslib.collection.history Closed (ID: 3)


Files in this item

This item appears in the following Collection(s)

Show simple item record