1. Açık Erişim@BUU
  2. İndeksli Yayınlar
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Please use this identifier to cite or link to this item: http://hdl.handle.net/11452/25292
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dc.contributor.authorMiti, G.K.-
dc.contributor.authorMoses, Anthony John-
dc.contributor.authorFox, David-
dc.date.accessioned2022-03-23T07:19:11Z-
dc.date.available2022-03-23T07:19:11Z-
dc.date.issued2003-01-
dc.identifier.citationMiti, G. K. vd. (2003). “A neural network-based tool for magnetic performance prediction of toroidal cores”. Journal of Magnetism and Magnetic Materials, 254(Special Issue), 262-264.en_US
dc.identifier.issn0304-8853-
dc.identifier.urihttps://doi.org/10.1016/S0304-8853(02)00788-6-
dc.identifier.urihttp://hdl.handle.net/11452/25292-
dc.descriptionBu çalışma, 05-07 Eylül 2001 tarihleri arasında Bilbao[İspanya]’da düzenlenen 15. International Symposium on Soft Magnetic Materials’da bildiri olarak sunulmuştur.tr_TR
dc.description.abstractGeometrical and building parameters have a strong influence on magnetic performance of wound toroidal cores made from electrical steel or similar strip products. This paper presents a neural network-based approach to predict losses and permeability in such cores of varying geometries over an induction range of 0.2-1.8T (50Hz). The approach is shown to be successful.en_US
dc.description.sponsorshipMCYT, Gobierno Espanolen_US
dc.description.sponsorshipEngineering and Physical Sciences Research Council GR/L36093/01en_US
dc.description.sponsorshipUniv Investigac, Dept Educen_US
dc.description.sponsorshipUniv Paris Vasco, Euskal Herriko Unibertsitateaen_US
dc.description.sponsorshipReal Soc Bascongada Amigos Paisen_US
dc.description.sponsorshipAgilent Technologiesen_US
dc.description.sponsorshipBFI, Optilasen_US
dc.language.isoenen_US
dc.publisherElsevieren_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectMaterials scienceen_US
dc.subjectPhysicsen_US
dc.subjectArtificial intelligenceen_US
dc.subjectMagnetic lossesen_US
dc.subjectNeural networksen_US
dc.subjectSoft magnetic materialsen_US
dc.subjectStrip-wound coresen_US
dc.subjectMagnetic leakageen_US
dc.subjectMagnetic permeabilityen_US
dc.subjectToroidal coresen_US
dc.subjectMagnetic coresen_US
dc.titleA neural network-based tool for magnetic performance prediction of toroidal coresen_US
dc.typeArticleen_US
dc.typeProceedings Paperen_US
dc.identifier.wos000180075600081tr_TR
dc.identifier.scopus2-s2.0-0037211428tr_TR
dc.relation.publicationcategoryKonferans Öğesi - Uluslararasıtr_TR
dc.contributor.departmentUludağ Üniversitesi/Fen-Edebiyat Fakültesi/Fizik Bölümü.tr_TR
dc.identifier.startpage262tr_TR
dc.identifier.endpage264tr_TR
dc.identifier.volume254tr_TR
dc.identifier.issueSpecial Issueen_US
dc.relation.journalJournal of Magnetism and Magnetic Materialsen_US
dc.contributor.buuauthorDerebaşı, Naim-
dc.relation.collaborationYurt dışıtr_TR
dc.subject.wosMaterials science, multidisciplinaryen_US
dc.subject.wosPhysics, condensed matteren_US
dc.indexed.wosSCIEen_US
dc.indexed.wosCPCISen_US
dc.indexed.scopusScopusen_US
dc.wos.quartileQ2 (Materials science, multidisciplinary)en_US
dc.wos.quartileQ3 (Physics, condensed matter)en_US
dc.contributor.scopusid11540936300tr_TR
dc.subject.scopusSilicon Steel; Soft Magnetic Materials; Ironen_US
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