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dc.contributor.authorKarban, Pavel
dc.contributor.authorPetrášová, Iveta
dc.contributor.authorDoležel, Ivo
dc.date.accessioned2023-02-06T11:00:22Z-
dc.date.available2023-02-06T11:00:22Z-
dc.date.issued2022
dc.identifier.citationKARBAN, P. PETRÁŠOVÁ, I. DOLEŽEL, I. DC Motor Benchmark with Prediction Based on Mixture of Experts. In 14th International Conference ELEKTRO, ELEKTRO 2022 : /proceedings/. Piscataway: IEEE, 2022. s. nestránkováno. ISBN: 978-1-66546-726-1 , ISSN: 2691-0616cs
dc.identifier.isbn978-1-66546-726-1
dc.identifier.issn2691-0616
dc.identifier.uri2-s2.0-85133959297
dc.identifier.urihttp://hdl.handle.net/11025/51328
dc.description.abstractThe Mixture of Experts (MoE)–based approach is applied to verify the possibility of using surrogate models for searching the optima of complex multicriteria problems with constraints. This approach can successfully solve problems when the design space is limited by a higher number of constraints and traditional methods of Design of Experiments (DoE) in conjunction with one surrogate model are not able to partition the design space acceptably enough for further prediction. The methodology is tested on a well-known DC motor benchmark, where the electromagnetic and temperature fields were solved analytically, in a simplified form.de
dc.format5 s.cs
dc.format.mimetypeapplication/pdf
dc.language.isoenen
dc.publisherIEEEen
dc.relation.ispartofseries14th International Conference ELEKTRO, ELEKTRO 2022 : /proceedings/en
dc.rightsPlný text je přístupný v rámci univerzity přihlášeným uživatelům.cs
dc.rights© IEEEen
dc.titleDC Motor Benchmark with Prediction Based on Mixture of Expertsen
dc.typekonferenční příspěvekcs
dc.typeConferenceObjecten
dc.rights.accessrestrictedAccessen
dc.type.versionpublishedVersionen
dc.description.abstract-translatedThe Mixture of Experts (MoE)–based approach is applied to verify the possibility of using surrogate models for searching the optima of complex multicriteria problems with constraints. This approach can successfully solve problems when the design space is limited by a higher number of constraints and traditional methods of Design of Experiments (DoE) in conjunction with one surrogate model are not able to partition the design space acceptably enough for further prediction. The methodology is tested on a well-known DC motor benchmark, where the electromagnetic and temperature fields were solved analytically, in a simplified form.en
dc.subject.translatedBrushless DC motoren
dc.subject.translatedanalytical modelen
dc.subject.translatedmixture of experts (MoE)en
dc.subject.translatedGaussian processen
dc.subject.translatedoptimizationen
dc.identifier.doi10.1109/ELEKTRO53996.2022.9803676
dc.type.statusPeer-revieweden
dc.identifier.obd43938099
dc.project.IDSGS-2021-011/Rozvoj technik snižování řádu systému v elektrotechnických aplikacíchcs
Appears in Collections:Konferenční příspěvky / Conference papers (RICE)
Konferenční příspěvky / Conference Papers (KEP)
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Please use this identifier to cite or link to this item: http://hdl.handle.net/11025/51328

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