电磁兼容的人工神经网络预测技术分析  

Artificial neural networks-based prediction of electromagnetic compatibility problems

作　　者：李永明[1] 祝言菊[1] 李旭[1] 俞集辉[1] 汪泉弟[1] 

机构地区：[1]重庆大学输配电装备及系统安全与新技术国家重点实验室,重庆400030

出　　处：《重庆大学学报（自然科学版）》2008年第11期1313-1316,1322,共5页Journal of Chongqing University

基　　金：重庆市自然科学基金重点资助项目(CSTC2006BA6015)

摘　　要：电磁兼容预测是实现电子设备或系统电磁兼容性(electromagnetic compatibility,EMC)的必要步骤。提出了应用人工神经网络对电磁兼容问题进行快速预测的方法。通过选择有效的电磁干扰参数作为输入预测因子,用误差反向传播的神经网络(back propagation,BP)构造输入预测因子与敏感设备骚扰响应之间的映射关系,并用电磁场数值计算方法获得的训练样本集和测试样本集对构造好的BP网络进行训练,建立了基于BP网络的电磁兼容快速预测模型。最后以导线间的串扰问题为预测算例,表明了该方法的有效性。It is necessary to predict electromagnetic compatibility （EMC） for electronic equipment and systems. We proposed a fast EMC prediction approach via artificial neural networks （ANN）. By choosing relevant electromagnetic interference parameters as the input prediction features, a back propagation （BP） neural network was used to construct the mapping between the input prediction features and the electromagnetic disturbance response of the sensitive system. The EMC fast prediction BP model was trained and tested by sample sets generated using an electromagnetic computational method. We used this method to predict the crosstalk coupling between two wires. The experimental results show the effectiveness of the proposed method.

关 键 词：电磁兼容 预测 人工神经网络 导线 串扰 

分 类 号：TM743[电气工程—电力系统及自动化]