基于卷积神经网络的天线阵列方向图合成

Jiaxuan Han, Xiaoli Wang, Yongfeng Wei, Yongliang Zhang
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引用次数: 0

摘要

本文提出了一种基于卷积神经网络(CNN)的智能计算方法,最后根据给定的相位分布推导出辐射方向图。在30*30超表面阵列上进行了验证,数值和实验结果与数值结果吻合较好。这一表现表明,机器可以使用深度卷积神经网络来“学习”电磁波的物理原理。训练后的神经网络可以在毫秒内预测所需的方向图,大大减少了计算时间。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Antenna Array Pattern Synthesize Based On Convolutional Neural Network
In this paper, an intelligent computing method based on convolutional neural network (CNN) is proposed, and finally the radiation pattern is derived from the given phase distribution. The method is validated on a 30*30 metasurface array and both numerical and experimental results are in good agreement with the numerical results. The performance shows that machines can use deep convolutional neural networks to “learn” the physical principles of electromagnetic waves. The trained neural network can predict the desired directional map in milliseconds, greatly reducing computation time.
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