An Artificial Neural Network Based Design of Triple-Band Microstrip Patch Antenna for WLAN Applications

被引：0
|
作者：
Wang, Jing Rui [1 ]
Liu, Wen Jie [1 ]
Tong, Mei Song [1 ]
机构：
[1] Tongji Univ, Shanghai Inst Intelligent Sci & Technol, Shanghai 201203, Peoples R China
来源：
2020 IEEE MTT-S INTERNATIONAL CONFERENCE ON NUMERICAL ELECTROMAGNETIC AND MULTIPHYSICS MODELING AND OPTIMIZATION (NEMO 2020) | 2020年
基金：
中国国家自然科学基金;
关键词：
Artificial neural network; multi-band antenna; microstrip patch antenna;
D O I：
10.1109/NEMO49486.2020.9343490
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：
With the development of communication system, multi-band antenna becomes more and more significant. In this paper, a triple-band microstrip patch antenna using artificial neural network techniques to optimize working bandwidth is proposed. The antenna is mainly composed of two metal patches and FR4 substrate. Three operating bands with the center frequency of 1.52 GHz (1.47 - 1.57 GHz), 2.46 GHz (2.43 - 2.48 GHz) and 2.79 GHz (2.78 - 2.81 GHz). Artificial neural network (ANN) model is trained and tested to optimize the bandwidth of the antenna, the simulation software HFSS is used to obtain 162 data sets of proposed antenna with four parameters related to patch dimension and substrate materials. According to the final test results, the average percentage error was within the acceptable range. The antenna also performs well in terms of gain. The antenna can be effectively used for WLAN applications.
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页数：4
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