Mode Recognition of Rectangular Dielectric Resonator Antenna Using Artificial Neural Network

被引：7

作者：

Xiao, Yuqi ^{[1
,2
,3
]}

Leung, Kwok Wa ^{[1
,2
,3
]}

Lu, Kai ^{[4
,5
]}

Leung, Chi-Sing ^{[2
]}

机构：

[1] City Univ Hong Kong, State Key Lab Terahertz & Millimeter Waves, Hong Kong, Peoples R China

[2] City Univ Hong Kong, Dept Elect Engn, Hong Kong, Peoples R China

[3] CityU Shenzhen Res Inst, Shenzhen Key Lab Millimeter Wave & Wideband Wirel, Shenzhen 518057, Peoples R China

[4] Sun Yat Sen Univ, Guangdong Prov Key Lab Optoelect Informat Proc Ch, Guangzhou 510006, Peoples R China

[5] Sun Yat Sen Univ, Sch Elect & Informat Technol, Guangzhou 510006, Peoples R China

来源：

IEEE TRANSACTIONS ON ANTENNAS AND PROPAGATION | 2022年 / 70卷 / 07期

关键词：

Artificial neural networks; Neurons; Training; Antenna measurements; Resonant frequency; Dielectric resonator antennas; Optical resonators; Artificial intelligence (AI); artificial neural network (ANN); dielectric resonator antenna (DRA); mode recognition; particle swarm optimization (PSO); resonant mode; PARTICLE SWARM OPTIMIZATION;

D O I：

10.1109/TAP.2022.3146860

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：

A new method powered by an artificial neural network (ANN) is studied for resonant-mode recognitions of a rectangular dielectric resonator antenna (DRA). Different rectangular DRAs were simulated with ANSYS HFSS to generate a large dataset for training the model. Their resonance frequencies, dimensions, and 3-D electric fields are input to the ANN. The output end is a 12-element array representing the corresponding probabilities of 12 different resonant modes. Using this trained ANN model, the mode recognition accuracy can reach 96.74%. Apart from identifying the resonant modes, our proposed approach can suggest how to modify a rectangular DRA to improve the purity of a resonant mode for better antenna performance.

引用

页码：5209 / 5216

页数：8

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