Identification of Bouc-Wen hysteretic systems using particle swarm optimization

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dc.contributor.author Charalampakis, AE en
dc.contributor.author Dimou, CK en
dc.date.accessioned 2014-03-01T01:33:37Z
dc.date.available 2014-03-01T01:33:37Z
dc.date.issued 2010 en
dc.identifier.issn 0045-7949 en
dc.identifier.uri http://hdl.handle.net/123456789/20492
dc.subject Bouc-Wen en
dc.subject Genetic algorithms en
dc.subject Hybrid methods en
dc.subject Hysteresis en
dc.subject Identification en
dc.subject PSO en
dc.subject.classification Computer Science, Interdisciplinary Applications en
dc.subject.classification Engineering, Civil en
dc.subject.other Bouc-Wen en
dc.subject.other Computational budget en
dc.subject.other Full scale en
dc.subject.other Hybrid method en
dc.subject.other Hysteretic systems en
dc.subject.other Identification en
dc.subject.other Nonlinear identifications en
dc.subject.other Particle swarm optimization algorithm en
dc.subject.other PSO en
dc.subject.other PSO algorithms en
dc.subject.other Welded steel connections en
dc.subject.other Competition en
dc.subject.other Genetic algorithms en
dc.subject.other Hysteresis en
dc.subject.other Mathematical operators en
dc.subject.other Stiffness en
dc.subject.other Particle swarm optimization (PSO) en
dc.title Identification of Bouc-Wen hysteretic systems using particle swarm optimization en
heal.type journalArticle en
heal.identifier.primary 10.1016/j.compstruc.2010.06.009 en
heal.identifier.secondary http://dx.doi.org/10.1016/j.compstruc.2010.06.009 en
heal.language English en
heal.publicationDate 2010 en
heal.abstract In this paper, two variants of the particle swarm optimization (PSO) algorithm are employed for the identification of Bouc-Wen hysteretic systems. The first variant is simple while the other is enhanced, as it implements additional operators. The algorithms are utilized for the identification of a Bouc-Wen hysteretic system that represents a full scale bolted-welded steel connection. The purpose of this work is to assess their comparative performance against other evolutionary algorithms in a highly non-linear identification problem on various levels of computational budget. The enhanced PSO algorithm outperforms its competitors in terms of both accuracy and robustness. (C) 2010 Elsevier Ltd. All rights reserved. en
heal.journalName Computers and Structures en
dc.identifier.doi 10.1016/j.compstruc.2010.06.009 en
dc.identifier.isi ISI:000288776600002 en
dc.identifier.volume 88 en
dc.identifier.issue 21-22 en
dc.identifier.spage 1197 en
dc.identifier.epage 1205 en

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