基于递归神经网络的TVS电磁脉冲响应建模  

EMP response modeling of TVS based on the recurrent neural network

作　　者：纪志强[1] 魏明[1] 吴启蒙 于毅成 

机构地区：[1]军械工程学院静电与电磁防护研究所,河北石家庄050003 [2]总装备部工程兵军事代表局驻武汉军事代表室,湖北武汉430073 [3]总装备部沈阳军事代表局驻哈尔滨地区代表室,黑龙江哈尔滨150000

出　　处：《河北科技大学学报》2015年第2期157-162,共6页Journal of Hebei University of Science and Technology

基　　金：国家自然科学基金(51277181)

摘　　要：针对传输线脉冲(TLP)测试方法实施过程工作量较大、测试结果与实际情况相符程度较差的问题,提出一种基于递归神经网络建模的电磁脉冲响应预测方法。该方法基于TLP测试系统,增加机器模型静电放电和人体金属模型静电放电两类注入电磁脉冲,分别建立Elman,Jordan神经网络以及它们的组合Elman-Jordan神经网络对NUP2105L型瞬态抑制二极管(TVS)进行建模,预测不同脉冲条件下TVS的响应。仿真结果表明,递归神经网络建模效果好、运算效率高。Due to the larger workload in the implementation process and the poor consistence between the test results and actual situation problems when using the transmission line pulse （TLP） testing methods, a modeling method based on the recurrent neural network is proposed for EMP response forecast. Based on the TLP testing system, two categories of EMP are increased, which are the machine model ESD EMP and human metal model ESD EMP. Elman neural network, Jordan neural net-work and their combination namely Elman-Jordan neural network are established for response modeling of NUP2105L transient voltage suppressor （TVS） forecasting the response under different EMP. The simulation results show that the recurrent neural network has satisfying modeling effects and high computation efficiency.

关 键 词：自动化技术应用 电磁脉冲 瞬态抑制二极管 系统辨识 递归神经网络 

分 类 号：O441.4[理学—电磁学]