基于双向门控循环单元神经网络的表面波天线金属方向图预测

2021 IEEE-APS Topical Conference on Antennas and Propagation in Wireless Communications (APWC) Pub Date : 2021-08-09 DOI:10.1109/APWC52648.2021.9539634

Jiashu Yang, K. Tong

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引用次数: 0

摘要

本文利用双向门控递归单元神经网络预测模型，提出了一种基于近场电场的表面波天线金属方向图预测方法。利用Bi-GRU神经网络模型对天线的金属方向图进行了预测，预测精度为100%，频率为34.5GHz。不同的均匀标记-空间比(MSR)对金属图案的预测精度没有影响。

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Metallic Pattern Prediction For Surface Wave Antennas Using Bidirectional Gated Recurrent Unit Neural Network

This work presents a surface wave antenna metallic pattern prediction from electric field in near-field by applying Bidirectional Gated Recurrent Unit neural network prediction model. The metallic pattern of the proposed antenna has been predicted by using Bi-GRU neural network model with prediction accuracy 100% at 34.5GHz. Different uniform mark-space-ratios (MSR) of the metallic pattern do not affect the metallic pattern prediction accuracy.

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2021 IEEE-APS Topical Conference on Antennas and Propagation in Wireless Communications (APWC)

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