Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/1435
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dc.contributorDepartment of Electronic and Information Engineering-
dc.contributorDepartment of Industrial and Systems Engineering-
dc.creatorLing, SH-
dc.creatorYeung, CW-
dc.creatorChan, KY-
dc.creatorIu, HHC-
dc.creatorLeung, FHF-
dc.date.accessioned2014-12-11T08:26:24Z-
dc.date.available2014-12-11T08:26:24Z-
dc.identifier.isbn1-4244-1340-0-
dc.identifier.urihttp://hdl.handle.net/10397/1435-
dc.language.isoenen_US
dc.publisherIEEEen_US
dc.rights© 2007 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.en_US
dc.rightsThis material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.en_US
dc.subjectAssociative storageen_US
dc.subjectConvergence of numerical methodsen_US
dc.subjectFunction evaluationen_US
dc.subjectNeural networksen_US
dc.subjectQuality controlen_US
dc.subjectWavelet transformsen_US
dc.titleA new hybrid particle swarm optimization with wavelet theory based mutation operationen_US
dc.typeConference Paperen_US
dc.description.otherinformationAuthor name used in this publication: H. H. C. Iuen_US
dc.description.otherinformationAuthor name used in this publication: F. H. F. Leungen_US
dc.description.otherinformationCentre for Signal Processing, Department of Electronic and Information Engineeringen_US
dc.description.otherinformationRefereed conference paperen_US
dcterms.abstractAn improved hybrid particle swarm optimization (PSO) that incorporates a wavelet-based mutation operation is proposed. It applies wavelet theory to enhance PSO in exploring solution spaces more effectively for better solutions. A suite of benchmark test functions and an application example on tuning an associative-memory neural network are employed to evaluate the performance of the proposed method. It is shown empirically that the proposed method outperforms significantly the existing methods in terms of convergence speed, solution quality and solution stability.-
dcterms.accessRightsopen accessen_US
dcterms.bibliographicCitationCEC 2007 : IEEE Congress on Evolutionary Computation, Singapore, 25–28 September 2007, p. 1977-1984-
dcterms.issued2007-
dc.identifier.scopus2-s2.0-56349165663-
dc.identifier.rosgroupidr38947-
dc.description.ros2007-2008 > Academic research: refereed > Refereed conference paper-
dc.description.oaVersion of Recorden_US
dc.identifier.FolderNumberOA_IR/PIRAen_US
dc.description.pubStatusPublisheden_US
Appears in Collections:Conference Paper
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