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dc.contributor.authorŠevčík, Jakub
dc.contributor.authorŠmídl, Václav
dc.contributor.authorVotava, Martin
dc.date.accessioned2023-02-06T11:00:18Z-
dc.date.available2023-02-06T11:00:18Z-
dc.date.issued2022
dc.identifier.citationŠEVČÍK, J. ŠMÍDL, V. VOTAVA, M. Identification of Thermal Model Parameters Using Deep Learning Techniques. In 2022 IEEE 31st International Symposium on Industrial Electronics (ISIE) : /proceedings/. Piscataway: IEEE, 2022. s. 978-981. ISBN: 978-1-66548-240-0 , ISSN: 2163-5145cs
dc.identifier.isbn978-1-66548-240-0
dc.identifier.issn2163-5145
dc.identifier.uri2-s2.0-85135786103
dc.identifier.urihttp://hdl.handle.net/11025/51295
dc.format4 s.cs
dc.format.mimetypeapplication/pdf
dc.language.isoenen
dc.publisherIEEEen
dc.relation.ispartofseries2022 IEEE 31st International Symposium on Industrial Electronics (ISIE) : /proceedings/en
dc.rightsPlný text je přístupný v rámci univerzity přihlášeným uživatelům.cs
dc.rights© IEEEen
dc.titleIdentification of Thermal Model Parameters Using Deep Learning Techniquesen
dc.typekonferenční příspěvekcs
dc.typeConferenceObjecten
dc.rights.accessrestrictedAccessen
dc.type.versionpublishedVersionen
dc.description.abstract-translatedIdentification of thermal model parameters using multi-step prediction is proposed. Even in the case of a linear model, the multi-step prediction is a non-linear complex function, hence we use techniques of deep learning for its identification. Specifically, we use stochastic gradient descent optimization with importance sampling of mini-batches. The importance function is designed to match the character of thermal experiments in which the step change is less frequent than steady-state operation. The proposed method is demonstrated on the identification of an IGBT module SK 20 DGDL 065 ET. The maximum error of the model identified by the multi-step approach is almost two times smaller than that of the model identified by the least squares.en
dc.subject.translateddeep learningen
dc.subject.translatedjunction temperatureen
dc.subject.translatedmultistep predictionen
dc.subject.translatedneural networken
dc.subject.translatedordinary least squaresen
dc.subject.translatedthermal modelen
dc.identifier.doi10.1109/ISIE51582.2022.9831641
dc.type.statusPeer-revieweden
dc.identifier.obd43936479
dc.project.IDEF18_069/0009855/Elektrotechnické technologie s vysokým podílem vestavěné inteligencecs
dc.project.IDSGS-2021-021/Výzkum a vývoj perspektivních technologií v elektrických pohonech a strojích IVcs
Appears in Collections:Konferenční příspěvky / Conference papers (RICE)
Konferenční příspěvky / Conference Papers (KEV)
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