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

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 neura...

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Bibliographic Details
Published inPhysics of atomic nuclei Vol. 86; no. 12; pp. 2696 - 2702
Main Authors Averyanov, G. P., Dmitrieva, V. V.
Format Journal Article
LanguageEnglish
Published Moscow Pleiades Publishing 01.12.2023
Springer Nature B.V
Subjects
Algorithms
Applications programs
Electrophysics
Machine learning
Neural networks
Parameters
Particle Accelerators for Nuclear Technologies
Particle and Nuclear Physics
Physics
Physics and Astronomy
Recognition
high-power pulsed devices
electrophysics
convolutional neural networks
forming and transmitting distributed parameter lines
machine learning algorithms
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Summary: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.
ISSN:1063-7788
1562-692X
DOI:10.1134/S1063778823100046