基于机器学习的高空电磁脉冲环境快速计算方法

doi:10.11896/jsjkx.220500046

Abstract

Abstract: The calculation of the environment of high altitude electromagnetic pulse is the basis of the electromagnetic pulse effect technology and the protection.In order to quickly calculate the high altitude electromagnetic pulse environment parameters and their distribution,a new method combining traditional calculation method with neural network method is proposed.Firstly,it calculates the HEMP discrete data with different explosive height,different equivalent and different position in space using the exis-ting method.Second,it establishes the fast multi-parameter calculation model using neural network based on these discrete data.Finally,this paper calculates the HEMP environment parameters of different explosive height,different equivalent and different position in space using the established model in parallel batch.The distribution of the high altitude electromagnetic pulse is also can be calculated quickly.Experimental results show that HEMP environment parameters and their distribution are accelerated using the fast method.It can provide a large number of incident field parameters for the calculation of high altitude electromagne-tic pulse conduction environment.

Key words: High-altitude electromagnetic pulse, Machine learning, Neural network, Fast calculation, Field distribution

CLC Number:

TP18

WANG Jinjin, CHENG Yinhui, NIE Xin, LIU Zheng. Fast Calculation Method of High-altitude Electromagnetic Pulse Environment Based on Machine Learning[J].Computer Science, 2023, 50(6A): 220500046-5.

References

[1]CHENG Y H,MA L,LI J X,et al.Frequency-domain method for determining HEMP standard waveform parameters[J].Modern Applied Physics,2014,5(2):135-139.
[2]WANG J G,NIU S L,ZHANG D H,et al.Altitude nu-clear explosion effect parameter book[M].Beijing:Atomic Energy Publishing House,2008:145-148.
[3]ALKHASAWNEH M S,TAY L T.A hybrid intelligent system integrating the cascade forward neural network with elman neural network[J].Arabian Journal for Science and Engineering,2018,43:6737-6749.
[4]HUNG S Y,LEE C Y,LIN Y L.Data science for delaminationprognosis and online batch learning in semiconductor Assembly Process[J].IEEE Transactions on Components,Packing and Manufacturing Technology,2020,10(2):314-324.
[5]KHADSE C B,CHAUDHARI M A,BORGHATE V B.Elec-tromagnetic compatibility estimator using scaled conjugate gradient backpropagation based artificial neural network[J].IEEE Transactions on Industrial Informatics,2017,13(3):1036-1045.
[6]CHEN H L,YE S Z,Modeling and Optimization of EMI Filter by Using Artificial Neural Network[J].IEEE Transactions on Electromagnetic Compatibility,2019,6(6):1979-1987.
[7]KUMAR R,SRIVASTAVA S,GUPTA J R P,et al.Comparative study of neural networks for dynamic nonlinear systems identification[J],Soft Computing,2019,23:101-114.
[8]HUANG J C,KO K,SHU M H,et al.Application and comparison of several machine learning algorithms and their integration models in regression problems[J].Neural Computing and Applications,2020,32:5461-5469.
[9]ZHANG W P,KUMAR M,LIU J Q.Multi-parameter online measurement IoT system based on BP neural network algorithm[J].Neural Computing and Applications,2019,31:8147-8155.
[10]GÜNE F,MAHOUTI P,DEMIREL S,et al.Cost-effectiveGRNN-based modeling of microwave transistors with a reduced number of measurements[J].International Journal of Numerical Modelling:Electronic Networks,Devices and Fields,2017,1:12.
[11]CHEN M.Matlab Neural Network and Exact Solution of Case[M].Beijing:Tsinghua University Press,2013:160-161.

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Fast Calculation Method of High-altitude Electromagnetic Pulse Environment Based on Machine Learning

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