Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/1405
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dc.contributorDepartment of Electronic and Information Engineering-
dc.creatorLing, SH-
dc.creatorLeung, FHF-
dc.creatorLam, HK-
dc.creatorLee, YS-
dc.creatorTam, PKS-
dc.date.accessioned2014-12-11T08:28:08Z-
dc.date.available2014-12-11T08:28:08Z-
dc.identifier.issn0278-0046-
dc.identifier.urihttp://hdl.handle.net/10397/1405-
dc.language.isoenen_US
dc.publisherInstitute of Electrical and Electronics Engineersen_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 algorithm (GA)en_US
dc.subjectNeural networken_US
dc.subjectShort-term load forecastingen_US
dc.titleA novel genetic-algorithm-based neural network for short-term load forecastingen_US
dc.typeJournal/Magazine Articleen_US
dc.description.otherinformationCentre for Multimedia Signal Processing, Department of Electronic and Information Engineeringen_US
dc.identifier.spage793-
dc.identifier.epage799-
dc.identifier.volume50-
dc.identifier.issue4-
dc.identifier.doi10.1109/TIE.2003.814869-
dcterms.abstractThis paper presents a neural network with a novel neuron model. In this model, the neuron has two activation functions and exhibits a node-to-node relationship in the hidden layer. This neural network provides better performance than a traditional feedforward neural network, and fewer hidden nodes are needed. The parameters of the proposed neural network are tuned by a genetic algorithm with arithmetic crossover and nonuniform mutation. Some applications are given to show the merits of the proposed neural network.-
dcterms.accessRightsopen accessen_US
dcterms.bibliographicCitationIEEE transactions on industrial electronics, Aug. 2003, v. 50, no. 4, p. 793-799-
dcterms.isPartOfIEEE transactions on industrial electronics-
dcterms.issued2003-08-
dc.identifier.isiWOS:000184376400021-
dc.identifier.scopus2-s2.0-0042525889-
dc.identifier.eissn1557-9948-
dc.identifier.rosgroupidr16157-
dc.description.ros2003-2004 > Academic research: refereed > Publication in refereed journal-
dc.description.oaVersion of Recorden_US
dc.identifier.FolderNumberOA_IR/PIRAen_US
dc.description.pubStatusPublisheden_US
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