Prediction of resonance frequencies of rectangular patch antenna using a multilayer perceptron network

Adil Bouhous
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Abstract

In this paper, a novel approach to accurately calculate the resonant frequencies of rectangular microstrip antennas using artificial neural networks (ANN) and the method of moments (MOM) is proposed. The ANN is developed to calculate the real part and the imaginary part of the complex resonant frequency of the antenna. The ANN is designed using multilayer perceptron network (MLP). Results concerning this resonance frequency as a function of the different physical and geometrical parameters of the antenna are presented. These obtained results correspond to the trained and tested data of the ANN model. A comparison with other results calculated from Chew's algorithm clearly shows the effectiveness of the proposed approach. The objective is to reduce the computational complexities, and thus to considerably reduce the computation time.
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基于多层感知器网络的矩形贴片天线谐振频率预测
本文提出了一种利用人工神经网络和矩量法精确计算矩形微带天线谐振频率的新方法。利用人工神经网络计算天线复谐振频率的实部和虚部。该人工神经网络采用多层感知器网络(MLP)进行设计。给出了谐振频率随天线物理和几何参数变化的结果。所得结果与人工神经网络模型的训练和测试数据相对应。通过与Chew算法计算结果的比较,可以清楚地看出该方法的有效性。目标是降低计算复杂性,从而大大减少计算时间。
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