Coupler Structure Optimization Using A Learning-transferred Modular Neural Network

被引:0
|
作者
Pan, Guangyuan [1 ]
Liu, Anlan [2 ]
Leng, Maoheng [3 ]
Yu, Ming [3 ]
机构
[1] Linyi Univ, Automat & Elect Engn, Linyi, Shandong, Peoples R China
[2] Chinese Univ Hong Kong, Elect Engn, Hong Kong, Peoples R China
[3] Southern Univ Sci & Technol, Elect & Elect Engn, Shenzhen, Guangdong, Peoples R China
来源
2021 IEEE 19TH INTERNATIONAL SYMPOSIUM ON ANTENNA TECHNOLOGY AND APPLIED ELECTROMAGNETICS (ANTEM) | 2021年
关键词
Coupler structure optimization; data driven; artificial intelligence; neural network;
D O I
10.1109/ANTEM51107.2021.9518653
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
In microwave components, efficient structure optimization of directional couplers is a challenging task Traditional methods, like space discretization and surrogate-based optimization, are time-consuming and lack of generalization ability. In this paper, by combing two latest AI techniques, namely transfer learning and modular neural network, a new approach is proposed. First, the framework with five submodules is designed, each submodule has a specific function Second, neural networks in submodules are trained using abundant simulated coupler data, and the deep relations between performance and structure for ideal couplers are learned. Third, the learned knowledge from submodules 2/4 is transferred to submodule 3/5, to provide optimization suggestions for couplers with unsatisfied performances. At last, an experiment using 75 four-port waveguide directional couplers is designed, the result show that the proposed method can effectively learn from the data and output the optimized structure with 91.67% improvement. It also indicates that the proposed method can outperform some other comparable methods.
引用
收藏
页数:2
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