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Original title:
Comparative Analysis of Gaussian Process Regression Modeling of an Induction Machine: Continuous vs. Mixed-Input Approaches
Authors: Bílek, Vladimír
Document type: Papers
Language: eng
Publisher: Vysoké učení technické v Brně, Fakulta elektrotechniky a komunikačních technologií
Abstract: This 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.
Keywords: Finite element method; Gaussian process regression; Induction machine; Machine learning; Mixed-Input surrogate models; Surrogate modeling
Host item entry: Proceedings II of the 30st Conference STUDENT EEICT 2024: Selected papers, ISBN 978-80-214-6230-4, ISSN 2788-1334
Institution: Brno University of Technology (web)
Document availability information: Fulltext is available in the Brno University of Technology Digital Library.
Original record: https://hdl.handle.net/11012/249321
Permalink: http://www.nusl.cz/ntk/nusl-622615
The record appears in these collections:
Universities and colleges > Public universities > Brno University of Technology
Conference materials > Papers
Authors: Bílek, Vladimír
Document type: Papers
Language: eng
Publisher: Vysoké učení technické v Brně, Fakulta elektrotechniky a komunikačních technologií
Abstract: This 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.
Keywords: Finite element method; Gaussian process regression; Induction machine; Machine learning; Mixed-Input surrogate models; Surrogate modeling
Host item entry: Proceedings II of the 30st Conference STUDENT EEICT 2024: Selected papers, ISBN 978-80-214-6230-4, ISSN 2788-1334
Institution: Brno University of Technology (web)
Document availability information: Fulltext is available in the Brno University of Technology Digital Library.
Original record: https://hdl.handle.net/11012/249321
Permalink: http://www.nusl.cz/ntk/nusl-622615
The record appears in these collections:
Universities and colleges > Public universities > Brno University of Technology
Conference materials > Papers
Record created 2024-07-21, last modified 2024-07-21