基于BP神经网络的电磁兼容性预测方法

Liu Hongyi, Luo Yunfeng, Xie Shuguo, Zhao Di
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引用次数: 0

摘要

为了更好地评估和预测电磁兼容性,本文提出了一种基于人工神经网络的方法。传统的BP神经网络存在许多固有的缺陷。随后,介绍了两种基于BP神经网络的改进方法:增加动量项的BP神经网络和自适应学习率的BP神经网络。分析了这两种方法在电磁兼容评价与预测中的可行性。最后进行了仿真实验,并对两种改进的BP神经网络进行了比较。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A BP neural network based prediction method for electromagnetic compatibility
In this paper, an artificial neural network based method is proposed in order to better evaluate and predict the electromagnetic compatibility. There are many inherent shortcomings in traditional BP neural networks. Thereafter, two improved BP neural network based method are introduced: BP neural networks with an additional momentum term and self-adaptive learning rate BP neural networks. The feasibility of applying these two methods to the evaluation and prediction of EMC. A simulation experiment is also given, together with the comparison of the two improved BP neural networks.
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