An intelligent swarm based-wavelet neural network for affective mobile phone design
| dc.contributor.author | Ling, S. | |
| dc.contributor.author | San, P. | |
| dc.contributor.author | Chan, Kit Yan | |
| dc.contributor.author | Leung, F. | |
| dc.contributor.author | Liu, Y. | |
| dc.date.accessioned | 2017-01-30T12:43:33Z | |
| dc.date.available | 2017-01-30T12:43:33Z | |
| dc.date.created | 2014-07-23T20:00:23Z | |
| dc.date.issued | 2014 | |
| dc.identifier.citation | Ling, 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.uri | http://hdl.handle.net/20.500.11937/24535 | |
| dc.identifier.doi | 10.1016/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.publisher | Elsevier BV | |
| dc.subject | Wavelet neural network | |
| dc.subject | New product development | |
| dc.subject | Fuzzy reasoning model | |
| dc.subject | Particle swarm optimization | |
| dc.subject | Affective design | |
| dc.title | An intelligent swarm based-wavelet neural network for affective mobile phone design | |
| dc.type | Journal Article | |
| dcterms.source.volume | 142 | |
| dcterms.source.startPage | 147 | |
| dcterms.source.endPage | 154 | |
| dcterms.source.issn | 0925-2312 | |
| dcterms.source.title | Neurocomputing | |
| 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.department | Department of Electrical and Computer Engineering | |
| curtin.accessStatus | Open access |