Neural networks-method of moments (NN-MoM) for the efficient filling of the coupling matrix
Neural networks-method of moments (NN-MoM) for the efficient filling of the coupling matrix
复制标题
用于有效填充耦合矩阵的神经网络矩量法 (NN-MoM)
DOI：
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发表时间：
2004
影响因子：
5.7
通讯作者：
Natalia K. Nikolova
中科院分区：
文献类型：
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作者：
Ezzeldin A. Soliman;Mohamed H. Bakr;Natalia K. Nikolova
本文提出了一种新的径向基函数神经网络（RBF-NN）模型，用于矩量法（MoM）耦合矩阵的有效填充。训练两个RBF-NN来计算耦合矩阵中的大多数元素。其余的元素使用传统的矩量法计算，因此该技术被称为神经网络矩量法（NN-MoM）。建议NN-MoM被施加到一些微带贴片天线阵列的分析。结果表明，NN-MoM是既准确又快速。该方法具有通用性，便于与矩量法平面求解器集成。
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.