Niankang Li, H. Yin, Zhuoyun Li, D. Jia, Zhang Shen, Wenxiang Wang, Yanyu Wei, Lingna Yue, Jin Xu, G. Zhao
{"title":"The Study of Traveling Wave Tube Large Signal Model Based on Machine Learning","authors":"Niankang Li, H. Yin, Zhuoyun Li, D. Jia, Zhang Shen, Wenxiang Wang, Yanyu Wei, Lingna Yue, Jin Xu, G. Zhao","doi":"10.1109/IRMMW-THz50926.2021.9567133","DOIUrl":null,"url":null,"abstract":"Driven by success in areas such as computer vision and natural language processing, attempts have been made in this work to combine deep learning with the large signal of the Traveling Wave Tube (TWT) to assist in predicting the output performance of the tube and advancing the pre-design of TWT. By feeding the trained artificial neural network with several feature parameters, the output power with the tube length can be predicted.","PeriodicalId":6852,"journal":{"name":"2021 46th International Conference on Infrared, Millimeter and Terahertz Waves (IRMMW-THz)","volume":"1 1","pages":"1-2"},"PeriodicalIF":0.0000,"publicationDate":"2021-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2021 46th International Conference on Infrared, Millimeter and Terahertz Waves (IRMMW-THz)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IRMMW-THz50926.2021.9567133","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
Abstract
Driven by success in areas such as computer vision and natural language processing, attempts have been made in this work to combine deep learning with the large signal of the Traveling Wave Tube (TWT) to assist in predicting the output performance of the tube and advancing the pre-design of TWT. By feeding the trained artificial neural network with several feature parameters, the output power with the tube length can be predicted.
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