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dc.contributor.authorLing, S.
dc.contributor.authorSan, P.
dc.contributor.authorChan, Kit Yan
dc.contributor.authorLeung, F.
dc.contributor.authorLiu, Y.
dc.date.accessioned2017-01-30T12:43:33Z
dc.date.available2017-01-30T12:43:33Z
dc.date.created2014-07-23T20:00:23Z
dc.date.issued2014
dc.identifier.citationLing, S. and San, P. and Chan, K.Y. and Leung, F. and Liu, Y. 2014. An intelligent swarm based-wavelet neural network for affective mobile phone design. Neurocomputing. 142: pp. 30-38.
dc.identifier.urihttp://hdl.handle.net/20.500.11937/24535
dc.identifier.doi10.1016/j.neucom.2014.01.054
dc.description.abstract

In this paper, an intelligent swarm based-wavelet neural network for affective mobile designed is presented. The contribution on this paper is to develop a new intelligent particle swarm optimization (iPSO), where a fuzzy logic system developed based on human knowledge is proposed to determine the inertia weight for the swarm movement of the PSO and the control parameter of a newly introduced cross-mutated operation. The proposed iPSO is used to optimize the parameters of wavelet neural network. An affective design of mobile phones is used to evaluate the effectiveness of the proposed iPSO. It has been found that significantly better results in a statistical sense can be obtained by the iPSO comparing with the existing hybrid PSO methods.

dc.publisherElsevier BV
dc.subjectWavelet neural network
dc.subjectNew product development
dc.subjectFuzzy reasoning model
dc.subjectParticle swarm optimization
dc.subjectAffective design
dc.titleAn intelligent swarm based-wavelet neural network for affective mobile phone design
dc.typeJournal Article
dcterms.source.volume142
dcterms.source.startPage147
dcterms.source.endPage154
dcterms.source.issn0925-2312
dcterms.source.titleNeurocomputing
curtin.note

NOTICE: this is the author’s version of a work that was accepted for publication in Neurocomputing. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Neurocomputing, Vol. 142 (2014). DOI: 10.1016/j.neucom.2014.01.054

curtin.departmentDepartment of Electrical and Computer Engineering
curtin.accessStatusOpen access


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