Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/1427
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dc.contributorDepartment of Electronic and Information Engineering-
dc.creatorLam, HK-
dc.creatorLeung, KF-
dc.creatorLing, SH-
dc.creatorLeung, FHF-
dc.creatorTam, PKS-
dc.date.accessioned2014-12-11T08:26:24Z-
dc.date.available2014-12-11T08:26:24Z-
dc.identifier.isbn0-7803-7280-8-
dc.identifier.urihttp://hdl.handle.net/10397/1427-
dc.language.isoenen_US
dc.publisherIEEEen_US
dc.rights© 2002 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.subjectAssociative storageen_US
dc.subjectFuzzy setsen_US
dc.subjectGenetic algorithmsen_US
dc.subjectKnowledge based systemsen_US
dc.subjectLearning systemsen_US
dc.subjectMembership functionsen_US
dc.subjectMultimedia systemsen_US
dc.titleOn interpretation of graffiti digits and commands for eBooks : neural fuzzy network and genetic algorithm approachen_US
dc.typeConference Paperen_US
dc.description.otherinformationAuthor name used in this publication: K. F. Leungen_US
dc.description.otherinformationAuthor name used in this publication: F. H. F. Leungen_US
dc.description.otherinformationAuthor name used in this publication: P. K. S. Tamen_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 proposed neural fuzzy network tuned by genetic algorithm (GA). By introducing a switch to each rule, the optimal number of rules can be learned. The membership functions of the neural fuzzy network are also tuned by GA. After training, the proposed neural fuzzy network is employed to interpret graffiti number inputs and commands for Electronic Books (eBooks).-
dcterms.accessRightsopen accessen_US
dcterms.bibliographicCitationFUZZ-IEEE'02 : proceedings of the 2002 IEEE International Conference on Fuzzy Systems : May 12-17, 2002, Honolulu, Hawaii, p. 443-448-
dcterms.issued2002-
dc.identifier.isiWOS:000177476600079-
dc.identifier.scopus2-s2.0-0036457943-
dc.relation.ispartofbookFUZZ-IEEE'02 : proceedings of the 2002 IEEE International Conference on Fuzzy Systems : May 12-17, 2002, Honolulu, Hawaii-
dc.relation.conferenceIEEE International Conference on Fuzzy Systems [FUZZ]-
dc.identifier.rosgroupidr10261-
dc.description.ros2001-2002 > 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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