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- W4384160670 abstract "This paper proposes a new approach for temperature regulation in rubber product manufacturing using a deep neural network-based controller. Traditionally, a PI temperature regulator is used to control the temperature during the heating process using high frequency resonant power converters. However, this method often leads to unsatisfactory results when operating conditions are altered under various disturbances, even with tuned PI coefficients. The proposed approach uses machine learning to construct a controller based on PI results as datasets. The obtained predicted model for the controller is very consistent and capable of offsetting all the drawbacks encountered in a classic PI regulator. The model is simulated using MATLAB, and the results show higher performance when exposed to various disturbances. This approach has the potential to be implemented in an embedded hardware for validation." @default.
- W4384160670 created "2023-07-14" @default.
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- W4384160670 date "2023-01-01" @default.
- W4384160670 modified "2023-09-24" @default.
- W4384160670 title "Machine Learning Assisted Resonant Inverter Control for Temperature Regulation in Rubber Manufacturing" @default.
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- W4384160670 doi "https://doi.org/10.1007/978-981-99-4484-2_19" @default.
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