| Autor: |
Marinko Kovačić, Ana Hanic, Zlatko Hanić |
| Rok vydání: |
2021 |
| Předmět: |
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| Zdroj: |
2021 International Conference on Electrical Drives & Power Electronics (EDPE). |
| DOI: |
10.1109/edpe53134.2021.9604055 |
| Popis: |
To increase the computational efficiency of electrical machine optimization and to utilize transfer learning from one metamodel to another, metamodels based on neural networks seem to be a promising solution. This paper presents a methodology of applying neural networks for developing metamodel for the prediction of interior permanent magnet machine performance. Furthermore, it provides procedures and guidelines on design space sampling and developing neural-network-based metamodels to achieve good predicting performance. The proposed approach has been tested on a case of a six-phase 200 kW IPM motor. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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