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Please use this identifier to cite or link to this item: http://acervodigital.unesp.br/handle/11449/130643
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dc.contributor.authorYang, Shiyou-
dc.contributor.authorMachado, Jose Marcio-
dc.contributor.authorNi, Guangzheng-
dc.contributor.authorHo, S. L.-
dc.contributor.authorZhou, Ping-
dc.date.accessioned2014-05-20T15:23:07Z-
dc.date.accessioned2016-10-25T21:21:40Z-
dc.date.available2014-05-20T15:23:07Z-
dc.date.available2016-10-25T21:21:40Z-
dc.date.issued2000-07-01-
dc.identifierhttp://dx.doi.org/10.1109/20.877611-
dc.identifier.citationIEEE Transactions on Magnetics. New York: IEEE-Inst Electrical Electronics Engineers Inc., v. 36, n. 4, p. 1004-1008, 2000.-
dc.identifier.issn0018-9464-
dc.identifier.urihttp://hdl.handle.net/11449/130643-
dc.identifier.urihttp://acervodigital.unesp.br/handle/11449/130643-
dc.description.abstractA self-learning simulated annealing algorithm is developed by combining the characteristics of simulated annealing and domain elimination methods. The algorithm is validated by using a standard mathematical function and by optimizing the end region of a practical power transformer. The numerical results show that the CPU time required by the proposed method is about one third of that using conventional simulated annealing algorithm.en
dc.format.extent1004-1008-
dc.language.isoeng-
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)-
dc.sourceScopus-
dc.subjectDomain elimination method-
dc.subjectElectromagnetic devices-
dc.subjectPower transformer-
dc.subjectSelf-learning ability-
dc.subjectSimulated annealing algorithms-
dc.subjectAlgorithms-
dc.subjectAnnealing-
dc.subjectOptimization-
dc.subjectElectromagnetic fields-
dc.titleA self-learning simulated annealing algorithm for global optimizations of electromagnetic devicesen
dc.typeoutro-
dc.contributor.institutionHong Kong Polytechnic University-
dc.contributor.institutionUniversidade Estadual Paulista (UNESP)-
dc.contributor.institutionZhejiang University-
dc.contributor.institutionAnsoft Corporation-
dc.description.affiliationHong Kong Polytech Univ, EE Dept, Hong Kong, Hong Kong, Peoples R China-
dc.description.affiliationSão Paulo State Univ, Dept Comp Sci & Stat, Sao Jose do Rio Preto, SP, Brazil-
dc.description.affiliationZhejiang Univ, EE Dept, Zhejiang, Peoples R China-
dc.description.affiliationAnsoft Corp, Pittsburgh, PA 15219 USA-
dc.description.affiliationUnespSão Paulo State Univ, Dept Comp Sci & Stat, Sao Jose do Rio Preto, SP, Brazil-
dc.identifier.doi10.1109/20.877611-
dc.identifier.wosWOS:000090067900084-
dc.rights.accessRightsAcesso restrito-
dc.relation.ispartofIEEE Transactions on Magnetics-
dc.identifier.scopus2-s2.0-0034217702-
Appears in Collections:Artigos, TCCs, Teses e Dissertações da Unesp

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