Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/1418
DC FieldValueLanguage
dc.contributorDepartment of Electronic and Information Engineering-
dc.creatorLing, SH-
dc.creatorLeung, FHF-
dc.creatorLam, HK-
dc.date.accessioned2014-12-11T08:26:24Z-
dc.date.available2014-12-11T08:26:24Z-
dc.identifier.isbn0-7803-9490-9-
dc.identifier.urihttp://hdl.handle.net/10397/1418-
dc.language.isoenen_US
dc.publisherIEEEen_US
dc.rights© 2006 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.subjectCodes (symbols)en_US
dc.subjectGenetic algorithmsen_US
dc.subjectParameter extractionen_US
dc.subjectPattern recognitionen_US
dc.subjectReal time systemsen_US
dc.titleA variable node-to-node-link neural network and its application to hand-written recognitionen_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 variable node-to-node-link neural network (VN²NN) trained by real-coded genetic algorithm (RCGA). The VN²NN exhibits a node-to-node relationship in the hidden layer, and the network parameters are variable. These characteristics make the network adapt to the changes of the input environment, enable it to tackle different input sets distributed in a large domain. Each input data set is effectively handled by a corresponding set of network parameters. The set of parameters are governed by the other nodes. Taking the advantage of these features, the proposed network ensures better learning and generalization abilities. Application of the proposed network to hand-written graffiti recognition will be presented so as to illustrate the improvement.-
dcterms.bibliographicCitationIJCNN '06 : 2006 International Joint Conference on Neural Networks : Vancouver, BC, Canada, July 16-21, 2006, p. 921-928-
dcterms.issued2006-
dc.identifier.isiWOS:000245125901057-
dc.identifier.scopus2-s2.0-40649103205-
dc.identifier.rosgroupidr30494-
dc.description.ros2006-2007 > Academic research: refereed > Refereed conference paper-
dc.description.oapublished_final-
Appears in Collections:Conference Paper
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