Potentials of machine learning in vacuum electronic devices demonstrated by the design of a magnetron injection gun

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Zhang, Liang and Cross, Adrian W. (2021) Potentials of machine learning in vacuum electronic devices demonstrated by the design of a magnetron injection gun. IEEE Transactions on Electron Devices, 68 (6). 3028 - 3033. ISSN 0018-9383 (https://doi.org/10.1109/TED.2021.3075166)

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Abstract

Great progress has been made on machine learning and its applications are expanding rapidly nowadays. Through the case study of optimizing a magnetron injection gun for gyrotron devices, the functions of machine learning were investigated by using two supervised learning algorithms, regression trees and artificial neural networks. They showed excellent performance in predicting the outputs, exploring the importance of the input parameters and the relationship with the output parameters. Machine learning can be a useful tool in the development of microwave vacuum electron devices.

ORCID iDs

Zhang, Liang ORCID logoORCID: https://orcid.org/0000-0002-6317-0395 and Cross, Adrian W. ORCID logoORCID: https://orcid.org/0000-0001-7672-1283;
  • Item type:Article
    ID code:76314
    Dates:
    Date
    Event
    1 June 2021
    Published
    5 May 2021
    Published Online
    20 April 2021
    Accepted
    Notes:© 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting /republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
    Subjects:Technology > Electrical engineering. Electronics Nuclear engineering
    Department:Faculty of Science > Physics
    Depositing user: Pure Administrator
    Date deposited:04 May 2021 15:40
    Last modified:11 Nov 2024 13:04
    URI:https://strathprints.strath.ac.uk/id/eprint/76314
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