Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/1357
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dc.contributorDepartment of Electronic and Information Engineering-
dc.creatorLing, SH-
dc.creatorLam, HK-
dc.creatorLeung, FHF-
dc.creatorLee, YS-
dc.date.accessioned2014-12-11T08:26:20Z-
dc.date.available2014-12-11T08:26:20Z-
dc.identifier.isbn0-7803-7906-3-
dc.identifier.urihttp://hdl.handle.net/10397/1357-
dc.language.isoenen_US
dc.publisherIEEEen_US
dc.rights© 2003 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.subjectGenetic algorithmen_US
dc.subjectNeural networken_US
dc.subjectPattern recognitionen_US
dc.subjectSunspot forecastingen_US
dc.titleA genetic algorithm based neural-tuned neural networken_US
dc.typeConference Paperen_US
dc.description.otherinformationCentre for Multimedia Signal Processing, Department of Electronic and Information Engineeringen_US
dc.description.otherinformationAuthor name used in this publication: F.H.F. Leungen_US
dc.description.otherinformationRefereed conference paperen_US
dcterms.abstractThis paper presents a neural-tuned neural network, which is trained by genetic algorithm (GA). The neural-tuned neural network consists of a neural network and a modified neural network. In the modified neural network, a neuron model with two activation functions is introduced. Some parameters of these activation functions will be tuned by neural network. The proposed network structure can increase the search space of the network and gives better performance than traditional feed-forward neural networks. Some application examples are given to illustrate the merits of the proposed network.-
dcterms.accessRightsopen accessen_US
dcterms.bibliographicCitationIECON'03 : the 29th annual conference of the IEEE Industrial Electronics Society : Roanoke, Virginia, USA, November 2nd (Sunday) to Thursday, November 6th (Thursday) 2003, p. 2423-2428-
dcterms.issued2003-
dc.identifier.scopus2-s2.0-1442358131-
dc.identifier.rosgroupidr20227-
dc.description.ros2003-2004 > 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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