A Neural Network Model to Predict the Radiation Resistance of Dipole Antenna

被引：0

作者：

Sahoo, Nihar K. ^{[1
]}

Gaul, Swapnil ^{[1
]}

Devikrishna, L. ^{[2
]}

Menon, S. Malavika ^{[2
]}

Sinha, Swapnil ^{[2
]}

Mohamed, Anu ^{[3
]}

机构：

[1] Numereg Pvt Ltd, Pune, India

[2] Intern Numereg Pvt Ltd, Dept ECE, GECBH, Trivandrum, India

[3] GECBH, Dept ECE, Trivandrum, India

来源：

2022 IEEE WIRELESS ANTENNA AND MICROWAVE SYMPOSIUM (WAMS 2022) | 2022年

关键词：

Dipole antenna; neural network; radiation resistance; DESIGN;

D O I：

10.1109/WAMS54719.2022.9847856

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 presented a multi-layer neural network model for the calculation of the radiation resistance of a dipole antenna. The network is trained using the data generated from the TaraNG solver. The data consist of radiation resistances and their corresponding frequencies. The proposed neural network structure is 1-20-20-20-1. The model contains one input layer, three hidden layers and one output layer. The predicted result is well agreed with TaraNG solver results.

引用

页数：3

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