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Radial Basis Function Neural Network With Hidden Node Interconnection Scheme for Thinned Array Modeling
IEEE Antennas and Wireless Propagation Letters ( IF 4.2 ) Pub Date : 2020-12-01 , DOI: 10.1109/lawp.2020.3034481
Li-Ye Xiao , Wei Shao , Fu-Long Jin , Bing-Zhong Wang , Qing Huo Liu

To extend the modeling area with artificial neural networks (ANNs) from finite periodic arrays to thinned arrays, where spacings between adjacent elements are crucial for array performance, an efficient model is proposed in this letter. Considering the spacings, a novel hidden node interconnection-radial basis function neural network (HNI–RBFNN) is developed to map the relationship between the array electromagnetic (EM) responses and the element ones. The element EM responses are obtained with the traditional RBFNN only involving the element geometry, while the connected weights of hidden layer nodes are determined by mutual coupling and array environment with the HNI scheme. A numerical example of the thinned phased array is used to evaluate the validity of the proposed model.

中文翻译:

用于细化阵列建模的具有隐藏节点互连方案的径向基函数神经网络

为了将人工神经网络 (ANN) 的建模区域从有限周期阵列扩展到细化阵列,其中相邻元素之间的间距对阵列性能至关重要,本文提出了一种有效模型。考虑到间距,开发了一种新颖的隐藏节点互连-径向基函数神经网络(HNI-RBFNN)来映射阵列电磁(EM)响应与单元响应之间的关系。使用传统的RBFNN 获得单元EM 响应,仅涉及单元几何,而隐藏层节点的连接权重由HNI 方案的互耦合和阵列环境决定。减薄相控阵的数值例子用于评估所提出模型的有效性。
更新日期:2020-12-01
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