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- W4312462064 abstract "As the traditional methods are not economical and hard to directly measure permanent magnet (PM) temperature of permanent magnet synchronous motor (PMSM), currently a reasonable and more popular consideration to measure rotor temperature is prediction by artificial intelligence (AI) methods. This paper proposes a generative adversarial network (GAN)-supported AI method to solve the PM temperature measurement problem. Firstly, this paper uses CTGAN to get generated dataset and combines it with the original dataset. Secondly, a new GAN-RF method based on random forest (RF) is proposed to predict PM temperature and the performance is compared with another popular method long short-term memory (LSTM). The advantage of the GAN-RF is improving the prediction accuracy of the RF model through the size extension of datasets and getting rid of the dependence of prediction work on time series models (LSTM, etc.) through GAN. The dataset collected by the LEA department at Paderborn university verifies the effectiveness of the proposed method." @default.
- W4312462064 created "2023-01-04" @default.
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- W4312462064 date "2022-01-01" @default.
- W4312462064 modified "2023-09-30" @default.
- W4312462064 title "Generative Adversarial Network-Supported Permanent Magnet Temperature Estimation by Using Random Forest" @default.
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- W4312462064 doi "https://doi.org/10.1007/978-981-19-3171-0_38" @default.
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