Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/1375
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dc.contributorDepartment of Electronic and Information Engineering-
dc.creatorLing, SH-
dc.creatorLeung, FHF-
dc.date.accessioned2014-12-11T08:26:23Z-
dc.date.available2014-12-11T08:26:23Z-
dc.identifier.isbn0-7803-9048-2-
dc.identifier.urihttp://hdl.handle.net/10397/1375-
dc.language.isoenen_US
dc.publisherIEEEen_US
dc.rights© 2005 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.subjectElectric load forecastingen_US
dc.subjectGenetic algorithmsen_US
dc.subjectParameter estimationen_US
dc.titleGenetic algorithm-based variable translation wavelet neural network and its applicationen_US
dc.typeConference Paperen_US
dc.description.otherinformationAuthor name used in this publication: F. H. F. Leungen_US
dc.description.otherinformation"Centre for Multimedia Signal Processing, Department of Electronic and Information Engineering"en_US
dc.description.otherinformationRefereed conference paperen_US
dcterms.abstractA variable translation wavelet neural network (VTWNN) trained by genetic algorithm is presented in this paper. In the proposed wavelet neural network, the translation parameters are variables depending on the network inputs. Thanks to the variable translation parameter, the network becomes an adaptive one, providing better performance and increased learning ability than conventional wavelet neural networks. Genetic algorithm is applied to train the parameters of the proposed wavelet neural network. An application example on short-term daily electric load forecasting in Hong Kong is presented to show the merits of the proposed network.-
dcterms.accessRightsopen accessen_US
dcterms.bibliographicCitation2005 IEEE International Joint Conference on Neural Networks (IJCNN) : Montreal, QC, Canada, July 31-August 4, 2005, p. 1365-1370-
dcterms.issued2005-
dc.identifier.isiWOS:000235178002006-
dc.identifier.scopus2-s2.0-33750141680-
dc.identifier.rosgroupidr25791-
dc.description.ros2005-2006 > Academic research: refereed > Refereed conference paper-
dc.description.oaVersion of Recorden_US
dc.identifier.FolderNumberOA_IR/PIRAen_US
dc.description.pubStatusPublisheden_US
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