基于神经网络的表面波天线金属单元方向图设计

Jiashu Yang, K. Tong
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引用次数: 0

摘要

本文提出了一种利用Wasserstein生成对抗网络(WGAN)和双向门控循环单元(Bi-GRU)神经网络模型,根据所需的远场辐射方向图生成表面波天线金属单元方向图的预测方法。在CST中对预测的金属单元图进行了三维建模,其辐射方向图与期望的输入辐射方向图的变化幅度小于1 dBi。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Surface Wave Antenna Metallic Cell Pattern Design Using Neural Network Method
This work presents a surface wave antenna metallic cell pattern prediction method which can be generated based on the required far-field radiation pattern by the mean of applying Wasserstein generative adversarial network (WGAN) and bi-directional gated recurrent unit (Bi-GRU) neural network models. The predicted metallic cell pattern has been 3D-modelled in CST and the radiation pattern shows less than 1 dBi variation level from the desired input radiation pattern.
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