An intelligent swarm based-wavelet neural network for affective mobile phone design

Ling, SH; San, PP; Chan, KY; Leung, FHF; Liu, Y

An intelligent swarm based-wavelet neural network for affective mobile phone design

Ling, SH

San, PP Chan, KY Leung, FHF Liu, Y

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Publication Type:: Journal Article
Citation:: Neurocomputing, 2014, 142 pp. 30 - 38
Issue Date:: 2014-10-22

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Field	Value	Language
dc.contributor.author	Ling, SH https://orcid.org/0000-0003-0849-5098	en_US
dc.contributor.author	San, PP	en_US
dc.contributor.author	Chan, KY	en_US
dc.contributor.author	Leung, FHF	en_US
dc.contributor.author	Liu, Y	en_US
dc.date.available	2020-05-25T19:17:23Z
dc.date.issued	2014-10-22	en_US
dc.identifier.citation	Neurocomputing, 2014, 142 pp. 30 - 38	en_US
dc.identifier.issn	0925-2312	en_US
dc.identifier.uri	http://hdl.handle.net/10453/33126
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. © 2014 Elsevier B.V.	en_US
dc.relation.ispartof	Neurocomputing	en_US
dc.relation.isbasedon	10.1016/j.neucom.2014.01.054	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	An intelligent swarm based-wavelet neural network for affective mobile phone design	en_US
dc.type	Journal Article
utslib.citation.volume	142	en_US
utslib.for	080108 Neural, Evolutionary and Fuzzy Computation	en_US
utslib.for	08 Information and Computing Sciences	en_US
utslib.for	09 Engineering	en_US
utslib.for	17 Psychology and Cognitive Sciences	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Biomedical Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - CHT - Health Technologies
utslib.copyright.status	open_access
pubs.publication-status	Published	en_US
pubs.volume	142	en_US

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. © 2014 Elsevier B.V.

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http://hdl.handle.net/10453/33126