Identification of Bouc-Wen hysteretic systems using particle swarm optimization

Charalampakis, AE; Dimou, CK

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	https://dspace.lib.ntua.gr/xmlui/handle/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.publisher	PERGAMON-ELSEVIER SCIENCE LTD	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