Application of Neural Networks to Analyses of Nonlinearly Loaded Antenna Arrays Including Mutual Coupling Effects
Resource
IEEE Transactions on Antennas and Propagation 53 (3): 1126-1132
Journal
IEEE Transactions on Antennas and Propagation
Journal Volume
53
Journal Issue
3
Pages
1126-1132
Date Issued
2005
Date
2005
Author(s)
Lee, Kun-Chou
Abstract
In this paper, radial basis functions based neural networks (RBF-NN) are applied to the scattering of finite and infinite nonlinearly loaded antenna arrays including mutual coupling effects. The nodes in the input layer represent the parameters of antenna arrays or magnitudes of incident fields. There exist some nodes in the hidden layer for nonlinear mapping. The nodes in the output layer represent the magnitude of voltage at the input terminals of antennas at different harmonic frequencies. Numerical examples show that the scattering responses predicted by the trained RBF-NN models are very consistent with those calculated from the harmonic balance techniques. The trained RBF-NN models for the scattering of nonlinearly loaded antenna arrays are very efficient and the array mutual coupling effects are included.
Type
journal article
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