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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.