基于机器学习的行波管大信号模型研究

2021 46th International Conference on Infrared, Millimeter and Terahertz Waves (IRMMW-THz) Pub Date : 2021-08-29 DOI:10.1109/IRMMW-THz50926.2021.9567133

Niankang Li, H. Yin, Zhuoyun Li, D. Jia, Zhang Shen, Wenxiang Wang, Yanyu Wei, Lingna Yue, Jin Xu, G. Zhao

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引用次数: 1

摘要

在计算机视觉和自然语言处理等领域的成功推动下，本研究尝试将深度学习与行波管(TWT)的大信号相结合，以帮助预测行波管的输出性能，并推进行波管的预设计。通过给训练好的人工神经网络输入多个特征参数，可以预测输出功率随管道长度的变化。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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The Study of Traveling Wave Tube Large Signal Model Based on Machine Learning

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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2021 46th International Conference on Infrared, Millimeter and Terahertz Waves (IRMMW-THz)

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