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dc.date.accessioned2021-12-10T08:45:28Z-
dc.date.available2021-12-10T08:45:28Z-
dc.date.issued2009-03-
dc.identifier.citationHacıismailoğlu, M. C. vd. (2009). "Prediction of dynamic hysteresis loops of nano-crystalline cores". Expert Systems with Applications, 36(2), Part 1, 2225-2227.en_US
dc.identifier.issn0957-4174-
dc.identifier.urihttps://doi.org/10.1016/j.eswa.2007.12.051-
dc.identifier.urihttps://www.sciencedirect.com/science/article/pii/S0957417407006422-
dc.identifier.urihttp://hdl.handle.net/11452/23167-
dc.description.abstractDynamic hysteresis loops of a range of nano-crystalline cores have been obtained over a wide frequency range (1-50 kHz). A dynamic hysteresis model front measurements using an artificial neural network trained by the delta-bar-delta learning algorithm has been developed. The input parameters include the geometrical dimensions of cores, peak magnetic induction and magnetizing frequency. The results show the neural network model has an acceptable estimation capability for dynamic hysteresis loops of toroidal nano-crystalline cores.en_US
dc.language.isoenen_US
dc.publisherPergamon-Elsevier Scienceen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectDynamic hysteresis modellingen_US
dc.subjectNano-crystalline coresen_US
dc.subjectNeural networken_US
dc.subjectToroidal coresen_US
dc.subjectComputer scienceen_US
dc.subjectEngineeringen_US
dc.subjectOperations research & management scienceen_US
dc.subjectCrystalline materialsen_US
dc.subjectHysteresis loopsen_US
dc.subjectMagnetic levitation vehiclesen_US
dc.subjectMagnetic materialsen_US
dc.subjectNeural networksen_US
dc.subjectDelta-bar-deltaen_US
dc.subjectDynamic hysteresis loopsen_US
dc.subjectDynamic hysteresis modelingen_US
dc.subjectDynamic hysteresis modellingen_US
dc.subjectGeometrical dimensionsen_US
dc.subjectNanocrystalline coresen_US
dc.subjectNeural network modelen_US
dc.subjectWide frequency rangeen_US
dc.subjectHysteresisen_US
dc.titlePrediction of dynamic hysteresis loops of nano-crystalline coresen_US
dc.typeArticleen_US
dc.identifier.wos000262178000122tr_TR
dc.identifier.scopus2-s2.0-56349165252tr_TR
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergitr_TR
dc.contributor.departmentUludağ Üniversitesi/Fen-Edebiyat Fakültesi/Fizik Anabilim Dalı.tr_TR
dc.relation.bap2002/4tr_TR
dc.contributor.orcid0000-0002-0781-3376tr_TR
dc.contributor.orcid0000-0003-2546-0022tr_TR
dc.identifier.startpage2225tr_TR
dc.identifier.endpage2227tr_TR
dc.identifier.volume36tr_TR
dc.identifier.issue2, Part 1tr_TR
dc.relation.journalExpert Systems with Applicationsen_US
dc.contributor.buuauthorHacıismailoğlu, Muhammed Cüneyt-
dc.contributor.buuauthorKüçük, İlker Semih-
dc.contributor.buuauthorDerebaşı, Naim-
dc.contributor.researcheridK-7950-2012tr_TR
dc.contributor.researcheridAAI-2254-2021tr_TR
dc.subject.wosComputer science, artificial intelligenceen_US
dc.subject.wosEngineering, electrical & electronicen_US
dc.subject.wosOperations research & management scienceen_US
dc.indexed.wosSCIEen_US
dc.indexed.scopusScopusen_US
dc.wos.quartileQ1en_US
dc.contributor.scopusid8975743500tr_TR
dc.contributor.scopusid6602910810tr_TR
dc.contributor.scopusid11540936300tr_TR
dc.subject.scopusSilicon Steel; Soft Magnetic Materials; Induction Motorsen_US
Koleksiyonlarda Görünür:Scopus
Web of Science

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