An Equivalent Radiation Source Based on Artificial Neural Network for EMI Prediction

S. Yao, Y. Shu, L. Tong, X.C. Wei, Y.B. Yang, E. Liu
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引用次数: 2

Abstract

In this paper, an equivalent radiation source based on the artificial neural network (ANN) is proposed for the electromagnetic interference (EMI) prediction of an unknown noise source. Firstly, the unknown noise source is equivalent to a dipole array, and the magnetic field over the plane above the unknown noise source is scanned. From this information a set of linear equations is obtained for the solution of the dipole array. Next, in order to consider the multi-reflections between the unknown source and its nearby components on the same PCB, and also the possible nonlinearity interaction between the circuits and electromagnetic fields, the original dipole array equivalent source is extended to the equivalent source based on the ANN. A numerical example shows that the proposed ANN equivalent source can be better in predicting the shadowing effect of the components around the unknown EMI source. This study provides a novel possible solution for the EMI source reconstruction through the near-field scanning.
基于人工神经网络的等效辐射源电磁干扰预测
本文提出了一种基于人工神经网络的等效辐射源,用于未知噪声源的电磁干扰预测。首先,将未知噪声源等效为偶极子阵列,扫描未知噪声源上方平面的磁场;根据这些信息,得到了一组求解偶极子阵列的线性方程。其次,为了考虑未知源与同一PCB上邻近元件之间的多重反射,以及电路与电磁场之间可能存在的非线性相互作用,将原偶极子阵列等效源扩展为基于人工神经网络的等效源。数值算例表明,所提出的人工神经网络等效源能够较好地预测未知电磁干扰源周围各分量的遮蔽效应。该研究为通过近场扫描重建电磁干扰源提供了一种新的可能解决方案。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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