Dynamic hysteresis modelling for nano-crystalline cores

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dc.contributor.buuauthorKüçük, İlker Semih
dc.contributor.buuauthorHacıismailoğlu, Muhammed Cüneyt
dc.contributor.buuauthorDerebaşı, Naim
dc.contributor.departmentUludağ Üniversitesi/Fen-Edebiyat Fakültesi/Fizik Bölümü.tr_TR
dc.contributor.orcid0000-0002-0781-3376tr_TR
dc.contributor.orcid0000-0003-2546-0022tr_TR
dc.contributor.researcheridK-7950-2012tr_TR
dc.contributor.researcheridAAI-2254-2021tr_TR
dc.contributor.scopusid6602910810tr_TR
dc.contributor.scopusid8975743500tr_TR
dc.contributor.scopusid11540936300tr_TR
dc.date.accessioned2022-03-18T08:42:04Z
dc.date.available2022-03-18T08:42:04Z
dc.date.issued2009-03
dc.description.abstractThis paper presents all artificial neural network approach based oil dynamic Preisach model to compute hysteresis loops of nano-crystalline cores. The network has been trained by a Levenberg-Marquardt learning algorithm. The model is fast and does not require tremendous computational efforts. The results obtained by using the proposed model are in good agreement with experimental results.en_US
dc.identifier.citationKüçük, İ. S. vd. (2009). "Dynamic hysteresis modelling for nano-crystalline cores". Expert Systems with Applications, 36(2), 3188-3190.en_US
dc.identifier.endpage3190tr_TR
dc.identifier.issn0957-4174
dc.identifier.issue2tr_TR
dc.identifier.scopus2-s2.0-56349090867tr_TR
dc.identifier.startpage3188tr_TR
dc.identifier.urihttps://doi.org/10.1016/j.eswa.2008.01.084
dc.identifier.urihttps://www.sciencedirect.com/science/article/pii/S0957417408000997
dc.identifier.urihttp://hdl.handle.net/11452/25185
dc.identifier.volume36tr_TR
dc.identifier.wos000262178100060tr_TR
dc.indexed.scopusScopusen_US
dc.indexed.wosSCIEen_US
dc.language.isoenen_US
dc.publisherPergamon-Elsevier Scienceen_US
dc.relation.bap2002/4tr_TR
dc.relation.journalExpert Systems with Applicationsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergitr_TR
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectDynamic hysteresis modelen_US
dc.subjectNano-crystalen_US
dc.subjectNeural networken_US
dc.subjectNeural-networken_US
dc.subjectGenetic algorithmen_US
dc.subjectToroidal coresen_US
dc.subjectPower lossesen_US
dc.subjectPredictionen_US
dc.subjectComputer scienceen_US
dc.subjectEngineeringen_US
dc.subjectOperations research & management scienceen_US
dc.subjectCrystalline materialsen_US
dc.subjectHysteresis loopsen_US
dc.subjectNeural networksen_US
dc.subjectArtificial neural network approachen_US
dc.subjectComputational efforten_US
dc.subjectDynamic hysteresis modelingen_US
dc.subjectDynamic hysteresis modellingen_US
dc.subjectLevenberg-Marquardt learning algorithmsen_US
dc.subjectNanocrystalline coresen_US
dc.subjectON dynamicsen_US
dc.subjectHysteresisen_US
dc.subject.scopusSilicon Steel; Soft Magnetic Materials; Ironen_US
dc.subject.wosComputer science, artificial intelligenceen_US
dc.subject.wosEngineering, electrical & electronicen_US
dc.subject.wosOperations research & management scienceen_US
dc.titleDynamic hysteresis modelling for nano-crystalline coresen_US
dc.typeArticle
dc.wos.quartileQ1en_US

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