偶极子天线辐射电阻预测的神经网络模型

2022 IEEE Wireless Antenna and Microwave Symposium (WAMS) Pub Date : 2022-06-05 DOI:10.1109/WAMS54719.2022.9847856

N. K. Sahoo, Swapnil Gaul, Devikrishna L, Malavika Menon S, Swapnil Sinha, A. Mohamed

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引用次数: 0

摘要

提出了一种计算偶极子天线辐射电阻的多层神经网络模型。网络使用TaraNG求解器生成的数据进行训练。数据包括辐射电阻及其相应的频率。提出的神经网络结构为1-20-20- 1。该模型包含一个输入层、三个隐藏层和一个输出层。预测结果与TaraNG求解结果吻合较好。

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

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.

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2022 IEEE Wireless Antenna and Microwave Symposium (WAMS)

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