A Parameter Search Method For Optimal Efficiency Design Of High Power Millimeter Wave Gyrotron TWT

被引：0

作者：

Zou, Fucheng ^{[1
]}

Yan, Ran ^{[1
]}

Luo, Yong ^{[1
]}

机构：

[1] Univ Elect Sci & Technol China, Sch Elect Sci & Engn, Chengdu 610054, Sichuan, Peoples R China

来源：

IVEC 2021: 2021 22ND INTERNATIONAL VACUUM ELECTRONICS CONFERENCE | 2021年

关键词：

Gyrotron; BP neural network; global optimization algorithm;

D O I：

10.1109/IVEC51707.2021.9722471

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

This paper introduces a method for searching the optimal efficiency parameters of high-power millimeter wave gyrotron based on BP neural network prediction. This method can establish the prediction model through the actual design gyrotron test data, and optimize the prediction model through the global optimization algorithm to obtain the optimal efficiency design parameters. This method can effectively extract accurate and effective information from the test data and feed back to the designers.

引用

页数：2

共 6 条

[1]

Goldberg DavidE., 2007, Genetic Algorithms in Search, Optimization Machine Learning

[2]

Huang Tao, 2017, VAC EL C IVEC 2017 I

[3] Automatic Hot Test of Gyrotron-Traveling Wave Tubes by Adaptive PID Feedback Control [J].

Liu, Guo ;

Wang, Zhaodong ;

Zhao, Guohui ;

Yan, Ran ;

Xu, Yong ;

Wang, Jianxun ;

Pu, Youlei ;

Jiang, Wei .

IEEE TRANSACTIONS ON ELECTRON DEVICES, 2017, 64 (03) :1302-1306

[4]

[孟祥逢 MENG Xiang-feng], 2010, [计算机工程与设计, Computer Engineering and Design], V31, P1550

[5]

Schaffer J. D., 1992, COGANN-92. International Workshop on Combinations of Genetic Algorithms and Neural Networks (Cat. No.92TH0435-8), P1, DOI 10.1109/COGANN.1992.273950

[6] A REVIEW OF EVOLUTIONARY ARTIFICIAL NEURAL NETWORKS [J].

YAO, X .

INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS, 1993, 8 (04) :539-567

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