Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/1392
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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.accessioned2014-12-11T08:26:23Z-
dc.date.available2014-12-11T08:26:23Z-
dc.identifier.isbn0-7803-7810-5-
dc.identifier.urihttp://hdl.handle.net/10397/1392-
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.subjectNeural Networken_US
dc.subjectNeural Fuzzy Networken_US
dc.subjectGenetic Algorithmen_US
dc.titleA genetic algorithm based fuzzy-tuned neural networken_US
dc.typeConference Paperen_US
dc.description.otherinformationAuthor name used in this publication: F. H. F. Leungen_US
dc.description.otherinformationAuthor name used in this publication: Y. S. Leeen_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 fuzzy-tuned neural network, which is trained by the genetic algorithm (GA). The fuzzy-tuned neural network consists of a neural-fuzzy network and a modified neural network. In the modified neural network, a novel neuron model with two activation functions is employed. The parameters of the proposed network are tuned by GA with arithmetic crossover and non-uniform mutation. Some application examples are given to illustrate the merits of the proposed network.-
dcterms.accessRightsopen accessen_US
dcterms.bibliographicCitationFUZZ-IEEE 2003 : proceedings of the 12th IEEE International Conference on Fuzzy Systems : Sunday 25 May-Wednesday 28 May, 2003, St. Louis, Missouri, USA, p. 220-225-
dcterms.issued2003-
dc.identifier.isiWOS:000183448800038-
dc.identifier.scopus2-s2.0-0038818485-
dc.relation.ispartofbookFUZZ-IEEE 2003 : proceedings of the 12th IEEE International Conference on Fuzzy Systems : Sunday 25 May-Wednesday 28 May, 2003, St. Louis, Missouri, USA-
dc.relation.conferenceIEEE International Conference on Fuzzy Systems [FUZZ]-
dc.identifier.rosgroupidr14875-
dc.description.ros2002-2003 > 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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