Machine Learning Based Prediction for the Response of Gas Discharge Tube to Damped Sinusoid Signal

Autor:	Jinjin Wang, Zhitong Cui, Zhiqiang Chen, Yayun Dong, Xin Nie
Jazyk:	angličtina
Rok vydání:	2022
Předmět:	pulse current injection gas discharge tube machine learning neural network damped sinusoid signal Technology
Zdroj:	Energies, Vol 15, Iss 7, p 2622 (2022)
Druh dokumentu:	article
ISSN:	15072622 1996-1073
DOI:	10.3390/en15072622
Popis:	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.
Databáze:	Directory of Open Access Journals
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