Recent Advances in Artificial neural networks for EM parameterized modeling and optimization

被引：0

作者：

Ma, Li ^{[1
]}

Zhang, Rattan ^{[2
]}

Yan, Shuxia ^{[3
]}

Zhang, Qijun ^{[4
]}

机构：

[1] Tianjin Univ, Sch Microelect, Tianjin, Peoples R China

[2] Southeast Univ, Sch Informat Sci & Engn, Nanjing, Peoples R China

[3] Carleton Univ, Dept Elect, Ottawa, ON, Canada

[4] Tiangong Univ, Sch Elect & Informat Engn, Tianjin, Peoples R China

来源：

2022 16TH EUROPEAN CONFERENCE ON ANTENNAS AND PROPAGATION (EUCAP) | 2022年

关键词：

Artificial neural network (ANN); electromagnetic (EM) optimization; parameterized modeling;

D O I：

暂无

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 reviews the recent advances in artificial neural networks (ANN) for electromagnetic (EM) parameterized modeling and optimization. As an advanced ANN-based EM parameterized modeling and optimization technique, the neurotransfer function (neuro-TF) is discussed further in this paper. The trained neuro-TF parameterized models can be further used for EM design optimization with repetitive geometrical variations.

引用

页数：3

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