Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/1352
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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/10397/1352-
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.subjectHand-written pattern recognitionen_US
dc.titleA genetic algorithm based variable structure neural networken_US
dc.typeConference Paperen_US
dc.description.otherinformationAuthor name used in this publication: F.H.F. Leungen_US
dc.description.otherinformationCentre for Multimedia Signal Processing, Department of Electronic and Information Engineeringen_US
dc.description.otherinformationRefereed conference paperen_US
dcterms.abstractThis paper presents a neural network model with a variable structure, which is trained by genetic algorithm (GA). The proposed neural network consists of a Neural Network with a Node-to-Node Relationship (N[sup 4]R) and a Network Switch Controller (NSC). In the N[sup 4]R, a modified neuron model with two activation functions in the hidden layer, and switches in its links are introduced. The NSC controls the switches in the N[sup 4]R. The proposed neural network can model different input patterns with variable network structures. The proposed neural network provides better result and learning ability than traditional feed forward neural networks. Two application examples on XOR problem and hand-written pattern recognition 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. 436-441-
dcterms.issued2003-
dc.identifier.isiWOS:000189465300075-
dc.identifier.scopus2-s2.0-1442304902-
dc.identifier.rosgroupidr17865-
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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