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Optimization and characterization of electromagnetically coupled patch antennas using RBF neural networks

机译:基于RBF神经网络的电磁耦合贴片天线的优化和表征

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摘要

A new neural network model is presented in this paper. It utilizes radial basis functions neural network. The model solves the problem of the electromagnetically coupled microstrip patch antennas. At a specific resonance frequency, the proposed model predicts the optimum geometrical dimensions of both the patch and feeding microstrip line. Moreover, it provides the important characteristics of the optimum design. These characteristics include the impedance bandwidth, gain, and radiation efficiency. The proposed neural network model is very accurate and extremely faster than the classical approach.
机译:本文提出了一种新的神经网络模型。它利用径向基函数神经网络。该模型解决了电磁耦合微带贴片天线的问题。在特定的共振频率下,提出的模型可预测贴片和馈电微带线的最佳几何尺寸。而且,它提供了优化设计的重要特征。这些特性包括阻抗带宽,增益和辐射效率。所提出的神经网络模型非常准确,并且比传统方法快得多。

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