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Computation of Resonant Frequency and Gain from Inset Fed Rectangular Shaped Microstrip Patch Antenna Using Deep Neural Network

机译:利用深层神经网络计算嵌入式Fed矩形微带贴片天线的谐振频率和增益。

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This paper presents a deep neural network to predict the design parameters of an inset-fed rectangular shaped microstrip patch antenna. A multilayer perceptron based deep neural network has been proposed to predict the resonant frequency and gain values of antenna. Using five hidden layers and mini-batch gradient descent model the presented network converged closely with 1.30% and 1.56% mean absolute percentage errors compared with simulated frequency and gain values respectively of validation dataset. Hence the designed model can be used to determine resonant frequency and gain of inset-fed rectangular shaped microstrip patch antenna with any given dimensions.
机译:本文提出了一个深层神经网络来预测嵌入式馈电矩形微带贴片天线的设计参数。提出了一种基于多层感知器的深度神经网络来预测天线的谐振频率和增益值。使用五个隐藏层和最小批量梯度下降模型,与分别验证数据集的模拟频率和增益值相比,所呈现的网络以1.30%和1.56%的平均绝对百分比误差紧密收敛。因此,所设计的模型可用于确定具有任何给定尺寸的嵌入式馈电矩形微带贴片天线的谐振频率和增益。

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