Neural networks-method of moments (NN-MoM) for the efficient filling of the coupling matrix

In this paper, novel radial basis function-neural network (RBF-NN) models are presented for the efficient filling of the coupling matrix of the method of moments (MoM). Two RBF-NNs are trained to calculate the majority of elements in the coupling matrix. The rest of elements are calculated using the...

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Bibliographic Details
Published in:IEEE transactions on antennas and propagation 2004-06, Vol.52 (6), p.1521-1529
Main Authors: Soliman, E.A., Bakr, M.H., Nikolova, N.K.
Format: Article
Language:English
Subjects:
Couplings
Filling
Integrated circuit technology
Microstrip antenna arrays
Microwave devices
Moment methods
Neural networks
Patch antennas
Radar antennas
Transmission line matrix methods
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Summary:In this paper, novel radial basis function-neural network (RBF-NN) models are presented for the efficient filling of the coupling matrix of the method of moments (MoM). Two RBF-NNs are trained to calculate the majority of elements in the coupling matrix. The rest of elements are calculated using the conventional MoM, hence the technique is referred to as neural network-method of moments (NN-MoM). The proposed NN-MoM is applied to the analysis of a number of microstrip patch antenna arrays. The results show that NN-MoM is both accurate and fast. The proposed technique is general and it is convenient to integrate with MoM planar solvers.
ISSN:0018-926X
1558-2221
DOI:10.1109/TAP.2004.829846