Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/1411
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dc.contributorDepartment of Electronic and Information Engineering-
dc.creatorLam, HK-
dc.creatorLing, SH-
dc.creatorLeung, KF-
dc.creatorLeung, FHF-
dc.date.accessioned2014-12-11T08:26:25Z-
dc.date.available2014-12-11T08:26:25Z-
dc.identifier.isbn0-7803-7293-X-
dc.identifier.urihttp://hdl.handle.net/10397/1411-
dc.language.isoenen_US
dc.publisherIEEEen_US
dc.rights© 2001 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.subjectCost effectivenessen_US
dc.subjectDigital arithmeticen_US
dc.subjectDigital devicesen_US
dc.subjectGenetic algorithmsen_US
dc.subjectLearning systemsen_US
dc.subjectProbabilityen_US
dc.titleOn interpretation of graffiti commands for eBooks using a neural network and an improved genetic algorithmen_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.otherinformationCentre for Multimedia Signal Processing, Department of Electronic and Information Engineeringen_US
dc.description.otherinformationRefereed conference paperen_US
dcterms.abstractThis paper presents the interpretation of graffiti commands for Electronic Books (eBooks). The interpretation process is achieved by training a proposed neural network (NN) with link switches using an improved genetic algorithm (GA). By introducing the switches to the links, the proposed NN can learn the optimal network structure automatically. The structure and the parameters of the NN are tuned by the improved GA, which is implemented by floating point numbers. The processing time of the improved GA is shorter as reflected by some benchmark test functions. Simulation results on interpreting graffiti commands for eBooks using the proposed NN with link switches and the improved GA will be shown.-
dcterms.accessRightsopen accessen_US
dcterms.bibliographicCitationThe 10th IEEE International Conference on Fuzzy Systems : meeting the grand challenge : machines that serve people : The University of Melbourne, Australia, December, 2001, Sunday 2nd to Wednesday 5th, p. 1464-1467-
dcterms.issued2001-
dc.identifier.isiWOS:000178178300362-
dc.identifier.scopus2-s2.0-0036343217-
dc.relation.ispartofbookThe 10th IEEE International Conference on Fuzzy Systems : meeting the grand challenge : machines that serve people : The University of Melbourne, Australia, December, 2001, Sunday 2nd to Wednesday 5th-
dc.relation.conferenceIEEE International Conference on Fuzzy Systems [FUZZ]-
dc.identifier.rosgroupidr10087-
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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