Deep-Learning-based Inverse Modelling with CMA-ES as Applied to the Design of A Wideband High-isolation Septum Polarizer

被引：1

作者：

Shu, Chao ^{[1
]}

Hu, Shaoqing ^{[1
]}

Chen, Xiaodong ^{[1
]}

Yao, Yuan ^{[2
]}

机构：

[1] Queen Mary Univ London, Sch Elect Engn & Comp Sci, London, England

[2] Beijing Univ Posts & Telecommun, Sch Elect Engn, Beijing, Peoples R China

来源：

2020 IEEE INTERNATIONAL SYMPOSIUM ON ANTENNAS AND PROPAGATION AND NORTH AMERICAN RADIO SCIENCE MEETING | 2020年

关键词：

septum polarizer; high isolation; wideband; deep learning; deep neural networks (DNNs); evolution strategy; OPTIMIZATION;

D O I：

10.1109/IEEECONF35879.2020.9330082

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 presents a design method for electromagnetic (EM) devices based on inverse modelling using deep neural networks (DNNs) and evolutionary algorithms, which is suitable for generating new designs for complex EM devices with many design parameters and multiple objectives at a reduced computational cost. This method has been successfully applied to the design of a W-band septum polarizer, achieving a high isolation > 40 dB over 15.8% bandwidth.

引用

页码：2001 / 2002

页数：2

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