基于现场测试数据的电磁兼容模型综合方法  

Techniques of Electromagnetic Compatibility Model Synthesis Based on Site Testing Data

作　　者：李高升[1] 林成龙[1] 迟名远[2] 刘培国[1] 

机构地区：[1]国防科技大学电子信息系统复杂电磁环境效应国家重点实验室 [2]中国人民解放军91860部队

出　　处：《微波学报》2016年第2期77-83,89,共8页Journal of Microwaves

基　　金：国家重大基础研究项目(613138);国家自然科学基金(61372029)

摘　　要：经常性的现场测试是掌握电子系统实时指标和电磁兼容状况的基本手段。针对电磁兼容测试数据较多但应用不充足的现状,提出了电磁兼容模型综合的概念,结合电磁兼容原理,实现了运用数理统计方法和人工神经网络技术对电磁测量结果进行数据挖掘,对统计分析方法进行了实用性设计,研究了神经网络训练方法的实用性及学习率确定方法。电磁兼容模型综合可建立或修正模型,归纳综合设备电磁参数及系统和平台电磁兼容性能变化规律,实现电磁兼容信息综合与状态预测,为增强系统电磁兼容针对性测试、主动管理和预知性维护与保障提供技术手段。Regular on-site testing is an elementary means to obtain real-time data and state of electromagnetic compatibility（EMC）of electronics systems. Nowadays,there is a lot of measured EMC data while the application of these data is insufficient. This paper puts forward the concept of EMC model synthesis. To carry out EMC data mining with measured electromagnetic data,we can build or modify models and synthesize variation rules of electromagnetic parameters of equipment and EMC performance of systems and platforms,then realize the information synthesis and state prediction. This paper studies the application of statistical algorithms and artificial neural network（ANN）in model synthesis. Operating flows and simulation results as well as measured data are presented.Relative research can support special measurement,active management and predictive maintenance, replenishment in the area of EMC.

关 键 词：电磁兼容维护 现场测试 模型综合 人工神经网络 

分 类 号：TN03[电子电信—物理电子学]