Identification of Thermal Model Parameters Using Deep Learning Techniques
| dc.contributor.author | Ševčík, Jakub | |
| dc.contributor.author | Šmídl, Václav | |
| dc.contributor.author | Votava, Martin | |
| dc.date.accessioned | 2023-02-06T11:00:18Z | |
| dc.date.available | 2023-02-06T11:00:18Z | |
| dc.date.issued | 2022 | |
| dc.description.abstract-translated | Identification 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.format | 4 s. | cs |
| dc.format.mimetype | application/pdf | |
| dc.identifier.citation | ŠEVČÍK, 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: 2163-5145 | cs |
| dc.identifier.doi | 10.1109/ISIE51582.2022.9831641 | |
| dc.identifier.isbn | 978-1-66548-240-0 | |
| dc.identifier.issn | 2163-5145 | |
| dc.identifier.obd | 43936479 | |
| dc.identifier.uri | 2-s2.0-85135786103 | |
| dc.identifier.uri | http://hdl.handle.net/11025/51295 | |
| dc.language.iso | en | en |
| dc.project.ID | EF18_069/0009855/Elektrotechnické technologie s vysokým podílem vestavěné inteligence | cs |
| dc.project.ID | SGS-2021-021/Výzkum a vývoj perspektivních technologií v elektrických pohonech a strojích IV | cs |
| dc.publisher | IEEE | en |
| dc.relation.ispartofseries | 2022 IEEE 31st International Symposium on Industrial Electronics (ISIE) : /proceedings/ | en |
| dc.rights | Plný text je přístupný v rámci univerzity přihlášeným uživatelům. | cs |
| dc.rights | © IEEE | en |
| dc.rights.access | restrictedAccess | en |
| dc.subject.translated | deep learning | en |
| dc.subject.translated | junction temperature | en |
| dc.subject.translated | multistep prediction | en |
| dc.subject.translated | neural network | en |
| dc.subject.translated | ordinary least squares | en |
| dc.subject.translated | thermal model | en |
| dc.title | Identification of Thermal Model Parameters Using Deep Learning Techniques | en |
| dc.type | konferenční příspěvek | cs |
| dc.type | ConferenceObject | en |
| dc.type.status | Peer-reviewed | en |
| dc.type.version | publishedVersion | en |
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