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Article: Modelling of a magneto-rheological damper by evolving radial basis function networks

TitleModelling of a magneto-rheological damper by evolving radial basis function networks
Authors
KeywordsGenetic Algorithms
Magneto-Rheological Dampers
Radial Basis Function Networks
Issue Date2006
PublisherElsevier Ltd. The Journal's web site is located at http://www.elsevier.com/locate/engappai
Citation
Engineering Applications Of Artificial Intelligence, 2006, v. 19 n. 8, p. 869-881 How to Cite?
AbstractThis paper presents an approach to approximate the forward and inverse dynamic behaviours of a magneto-rheological (MR) damper using evolving radial basis function (RBF) networks. Due to the highly nonlinear characteristics of MR dampers, modelling of MR dampers becomes a very important problem to their applications. In this paper, an alternative representation of the MR damper in terms of evolving RBF networks, which have a structure of four input neurons and one output neuron to emulate the forward and inverse dynamic behaviours of an MR damper, respectively, is developed by combining the genetic algorithms (GAs) to search for the network centres with other standard learning algorithms. Training and validating of the evolving RBF network models are achieved by using the data generated from the numerical simulation of the nonlinear differential equations proposed for the MR damper. It is shown by the validation tests that the evolving RBF networks can represent both forward and inverse dynamic behaviours of the MR damper satisfactorily. © 2006 Elsevier Ltd. All rights reserved.
Persistent Identifierhttp://hdl.handle.net/10722/156855
ISSN
2015 Impact Factor: 2.368
2015 SCImago Journal Rankings: 1.371
ISI Accession Number ID
References

 

DC FieldValueLanguage
dc.contributor.authorDu, Hen_US
dc.contributor.authorLam, Jen_US
dc.contributor.authorZhang, Nen_US
dc.date.accessioned2012-08-08T08:44:17Z-
dc.date.available2012-08-08T08:44:17Z-
dc.date.issued2006en_US
dc.identifier.citationEngineering Applications Of Artificial Intelligence, 2006, v. 19 n. 8, p. 869-881en_US
dc.identifier.issn0952-1976en_US
dc.identifier.urihttp://hdl.handle.net/10722/156855-
dc.description.abstractThis paper presents an approach to approximate the forward and inverse dynamic behaviours of a magneto-rheological (MR) damper using evolving radial basis function (RBF) networks. Due to the highly nonlinear characteristics of MR dampers, modelling of MR dampers becomes a very important problem to their applications. In this paper, an alternative representation of the MR damper in terms of evolving RBF networks, which have a structure of four input neurons and one output neuron to emulate the forward and inverse dynamic behaviours of an MR damper, respectively, is developed by combining the genetic algorithms (GAs) to search for the network centres with other standard learning algorithms. Training and validating of the evolving RBF network models are achieved by using the data generated from the numerical simulation of the nonlinear differential equations proposed for the MR damper. It is shown by the validation tests that the evolving RBF networks can represent both forward and inverse dynamic behaviours of the MR damper satisfactorily. © 2006 Elsevier Ltd. All rights reserved.en_US
dc.languageengen_US
dc.publisherElsevier Ltd. The Journal's web site is located at http://www.elsevier.com/locate/engappaien_US
dc.relation.ispartofEngineering Applications of Artificial Intelligenceen_US
dc.subjectGenetic Algorithmsen_US
dc.subjectMagneto-Rheological Dampersen_US
dc.subjectRadial Basis Function Networksen_US
dc.titleModelling of a magneto-rheological damper by evolving radial basis function networksen_US
dc.typeArticleen_US
dc.identifier.emailLam, J:james.lam@hku.hken_US
dc.identifier.authorityLam, J=rp00133en_US
dc.description.naturelink_to_subscribed_fulltexten_US
dc.identifier.doi10.1016/j.engappai.2006.02.005en_US
dc.identifier.scopuseid_2-s2.0-33750509151en_US
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-33750509151&selection=ref&src=s&origin=recordpageen_US
dc.identifier.volume19en_US
dc.identifier.issue8en_US
dc.identifier.spage869en_US
dc.identifier.epage881en_US
dc.identifier.isiWOS:000242694900004-
dc.publisher.placeUnited Kingdomen_US
dc.identifier.scopusauthoridDu, H=7201901161en_US
dc.identifier.scopusauthoridLam, J=7201973414en_US
dc.identifier.scopusauthoridZhang, N=7401648302en_US

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