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Application of Machine Learning Algorithms and Neural Networks for Recognition of Parasitic Parameters by the Output Signal in High-Power Pulsed Electrophysics Devices

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Abstract

The problem of recognition and classification of loads at the output of generating and transmitting distributed parameter lines (DPL) in devices of high-power pulse technology (HPPT) by the amplitude and shape of the output signal using mathematical models based on machine learning methods and neural networks has been considered. A web application that recognizes parasitic parameters occurring in the devices based on DPL has been developed.

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Funding

This work was supported by ongoing institutional funding. No additional grants to carry out or direct this particular research were obtained.

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  1. National Research Nuclear University MEPhI (Moscow Engineering Physics Institute), 115409, Moscow, Russia

    G. P. Averyanov & V. V. Dmitrieva

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  1. G. P. Averyanov
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  2. V. V. Dmitrieva
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Correspondence to G. P. Averyanov or V. V. Dmitrieva.

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Translated by N. Petrov

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Averyanov, G.P., Dmitrieva, V.V. Application of Machine Learning Algorithms and Neural Networks for Recognition of Parasitic Parameters by the Output Signal in High-Power Pulsed Electrophysics Devices. Phys. Atom. Nuclei 86, 2696–2702 (2023). https://doi.org/10.1134/S1063778823100046

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