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 |
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Jazyk: | angličtina |
Rok vydání: | 2022 |
Předmět: | |
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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