EMP response modeling of TVS based on the recurrent neural network

• Main Authors: Zhiqiang JI, Ming WEI, Qimeng WU, Yicheng YU
• Format: Article
• Language: zho
• Published: Hebei University of Science and Technology 2015-04-01
• Series: Journal of Hebei University of Science and Technology
• Subjects: application of automation technology; electromagnetic pulse (EMP); transient voltage suppressor (TVS); system identification; recurrent neural network
• Online Access: http://xuebao.hebust.edu.cn/hbkjdx/ch/reader/create_pdf.aspx?file_no=b201502007&flag=1&journal_

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 network 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.