Comparative Analysis of Gaussian Process Regression Modeling of an Induction Machine: Continuous vs. Mixed-Input Approaches

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but.event.date23.04.2024cs
but.event.titleSTUDENT EEICT 2024cs
dc.contributor.authorBílek, Vladimír
dc.date.accessioned2024-07-09T07:47:51Z
dc.date.available2024-07-09T07:47:51Z
dc.date.issued2024cs
dc.description.abstractThis paper investigates the application of machine learning technique for modeling continuous and mixed-input parameters of electrical machines. The design of electrical machines typically requires the consideration of certain parameters as integer values due to their physical significance, including the number of stator/rotor slots, stator wires, and rotor bars. Traditional machine learning methods, which predominantly treat input parameters as purely continuous, may compromise modeling accuracy for such applications. To address this challenge, models capable of handling mixed-input parameters were used for the case study. Two training datasets were generated: one with purely continuous inputs and another with both continuous inputs and a categorical parameter, specifically, the number of stator conductors. Gaussian process regression was employed to build three models: two with continuous kernels, trained on both datasets, and one with a mixed kernel, trained only on the dataset containing a categorical parameter. A comparative analysis, demonstrated on a 1.5 kW induction machine - though applicable to a wide range of machines - illustrates the differences between the proposed approaches. The results highlight the importance of selecting an appropriate model for the Multi- Objective Bayesian optimization of electrical machines.en
dc.formattextcs
dc.format.extent227-231cs
dc.format.mimetypeapplication/pdfen
dc.identifier.citationProceedings II of the 30st Conference STUDENT EEICT 2024: Selected papers. s. 227-231. ISBN 978-80-214-6230-4cs
dc.identifier.doi10.13164/eeict.2024.227
dc.identifier.isbn978-80-214-6230-4
dc.identifier.issn2788-1334
dc.identifier.urihttps://hdl.handle.net/11012/249321
dc.language.isoencs
dc.publisherVysoké učení technické v Brně, Fakulta elektrotechniky a komunikačních technologiícs
dc.relation.ispartofProceedings II of the 30st Conference STUDENT EEICT 2024: Selected papersen
dc.relation.urihttps://www.eeict.cz/eeict_download/archiv/sborniky/EEICT_2024_sbornik_2.pdfcs
dc.rights© Vysoké učení technické v Brně, Fakulta elektrotechniky a komunikačních technologiícs
dc.rights.accessopenAccessen
dc.subjectFinite element methoden
dc.subjectGaussian process regressionen
dc.subjectInduction machineen
dc.subjectMachine learningen
dc.subjectMixed-Input surrogate modelsen
dc.subjectSurrogate modelingen
dc.titleComparative Analysis of Gaussian Process Regression Modeling of an Induction Machine: Continuous vs. Mixed-Input Approachesen
dc.type.driverconferenceObjecten
dc.type.statusPeer-revieweden
dc.type.versionpublishedVersionen
eprints.affiliatedInstitution.departmentFakulta elektrotechniky a komunikačních technologiícs
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