A Neural-Network Based Model of the Magnetic Nonlinearity of a DC Electromagnet

J. M. Díaz-Chacón, R. B. B. Ovando-Martinez, C. Hernández, M. Arjona
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Abstract

This paper shows how an Artificial Neural Network model (ANN) can be used to fit the nonlinear magnetic behavior of a DC electromagnet. An ANN model is trained to obtain a generalized function of the B2-?r curve, which is commonly used in an electromagnetic model. Once the generalized function and its derivative are obtained, they are used to solve a magnetostatic nonlinear problem of a DC device using the finite element method and the Newton-Raphson algorithm.
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基于神经网络的直流电磁铁磁非线性模型
本文介绍了如何利用人工神经网络模型来拟合直流电磁铁的非线性磁性行为。训练一个人工神经网络模型来获得B2-?R曲线,通常用于电磁模型。得到广义函数及其导数后,利用有限元法和Newton-Raphson算法求解直流器件的静磁非线性问题。
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