Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/862
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dc.contributorDepartment of Electrical Engineering-
dc.creatorShi, KL-
dc.creatorChan, TF-
dc.creatorWong, YK-
dc.creatorHo, SL-
dc.date.accessioned2014-12-11T08:24:27Z-
dc.date.available2014-12-11T08:24:27Z-
dc.identifier.issn0093-9994-
dc.identifier.urihttp://hdl.handle.net/10397/862-
dc.language.isoenen_US
dc.publisherInstitute of Electrical and Electronics Engineersen_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.subjectDirect self controlen_US
dc.subjectInduction motor driveen_US
dc.subjectMatlab/Simulinken_US
dc.subjectNeural networksen_US
dc.titleDirect self control of induction motor based on neural networken_US
dc.typeJournal/Magazine Articleen_US
dc.description.otherinformationAuthor name used in this publication: Y. K. Wongen_US
dc.description.otherinformationAuthor name used in this publication: S. L. Hoen_US
dc.identifier.spage1290-
dc.identifier.epage1298-
dc.identifier.volume37-
dc.identifier.issue5-
dc.identifier.doi10.1109/28.952504-
dcterms.abstractThis paper presents an artificial-neural-network-based direct-self-control (ANN-DSC) scheme for an inverter-fed three-phase induction motor. In order to cope with the complex calculations required in direct self control (DSC), the proposed artificial-neural-network (ANN) system employs the individual training strategy with fixed-weight and supervised models. A computer simulation program is developed using Matlab/Simulink together with the Neural Network Toolbox. The simulated results obtained demonstrate the feasibility of ANN-DSC. Compared with the classical digital-signal-processor-based DSC, the proposed ANN-based scheme incurs much shorter execution times and, hence, the errors caused by control time delays are minimized.-
dcterms.accessRightsopen accessen_US
dcterms.bibliographicCitationIEEE transactions on industry applications, Sept./Oct. 2001, v. 37, no. 5, p. 1290-1298-
dcterms.isPartOfIEEE transactions on industry applications-
dcterms.issued2001-09-
dc.identifier.isiWOS:000171216100010-
dc.identifier.scopus2-s2.0-0035439136-
dc.identifier.eissn1939-9367-
dc.identifier.rosgroupidr07037-
dc.description.ros2001-2002 > 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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