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Machine Learning Model for High-Frequency Magnetic Loss Predictions Based on Loss Map by a Measurement Kit
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| Author(s) |
Xiaobing SHEN, Wilmar MARTINEZ |
| Abstract |
Accurately forecasting the losses in highfrequencymagnetic materials is a significant challengewhen optimizing the design of high-frequency (HF) magneticcomponents. Existing models do not adequatelyconsider the intricate interactions among geometry, andtemperature factors, which have distinct and substantialimpacts on core losses. A new method is introduced, whichutilizes a Deep Neural Network (DNN) model to constructparameterized models for high-frequency magnetic coreloss based on measurement data. The DNN employs theGaussian Error Linear Unit (GELU) activation functionand Huber loss function, and its performance is comparedto that of a conventional Rectified Linear Unit (ReLU) activationand Mean Squared Error (MSE) loss function. Theproposed DNN demonstrates significantly higher accuracyand improved robustness. |
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| Filename: | 0297-epe2023-full-13382272.pdf |
| Filesize: | 3.911 MB |
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| Type |
Members Only |
| Date |
Last modified 2023-09-24 by System |
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