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- W4385956617 abstract "Hysteresis is a fundamental phenomenon observed in ferromagnetic materials, characterized by the lagging of the magnetization response behind the applied magnetic field. The Jiles-Atherton (JA) model has gained popularity due to its ability to reproduce major and minor loops with relatively few parameters and its ease of implementation. However, the JA model exhibits limitations in accurately simulating hysteresis loops in low-saturation regions. Therefore, this paper proposes the use of neural networks to enhance the accuracy of hysteresis loop simulations in low-saturation regions. Neural networks are trained on experimental data and are capable of capturing the underlying nonlinear relationships between the applied field and magnetization response. The integration of neural networks into the JA model significantly improves the simulation accuracy in low saturation regions, making it more suitable for the simulation of transformer low-frequency transients. This study demonstrates the potential of leveraging advanced machine learning techniques to refine classical models and expand their applicability." @default.
- W4385956617 created "2023-08-18" @default.
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- W4385956617 date "2023-06-16" @default.
- W4385956617 modified "2023-10-17" @default.
- W4385956617 title "Improving the Accuracy of Jiles-Atherton Hysteresis Loop Cluster Simulations in Low Saturation Regions with Neural Networks" @default.
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- W4385956617 doi "https://doi.org/10.1145/3608251.3608268" @default.
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