Machine Learning Based Prediction for the Response of Gas Discharge Tube to Damped Sinusoid Signal
Jinjin Wang,
Zhitong Cui,
Zhiqiang Chen,
Yayun Dong and
Xin Nie
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Jinjin Wang: State Key Laboratory of Intense Pulsed Radiation Simulation and Effect, Northwest Institute of Nuclear Technology, Xi’an 710024, China
Zhitong Cui: State Key Laboratory of Intense Pulsed Radiation Simulation and Effect, Northwest Institute of Nuclear Technology, Xi’an 710024, China
Zhiqiang Chen: State Key Laboratory of Intense Pulsed Radiation Simulation and Effect, Northwest Institute of Nuclear Technology, Xi’an 710024, China
Yayun Dong: State Key Laboratory of Intense Pulsed Radiation Simulation and Effect, Northwest Institute of Nuclear Technology, Xi’an 710024, China
Xin Nie: State Key Laboratory of Intense Pulsed Radiation Simulation and Effect, Northwest Institute of Nuclear Technology, Xi’an 710024, China
Energies, 2022, vol. 15, issue 7, 1-9
Abstract:
In order to predict the circuit response of a Gas Discharge Tube (GDT) to an electromagnetic pulse, a “black box” model for a GDT based on a machine learning method is proposed and validated in this paper.Firstly, the machine learning model of the Elman neural network is established by taking advantage of the existing measurement data to dampen the sinusoid signal, and then the established model is adopted to predict the response waveform of an unknown injection current grade and frequency.Without considering the complex physical parameters and dynamic behavior of GDTs, the Elman neural network modeling method is simpler than the existing physical or Pspice model.Validation experiments show a good agreement between the predicted and the measured waveforms.
Keywords: pulse current injection; gas discharge tube; machine learning; neural network; damped sinusoid signal (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (1)
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