基于双级神经网络的多形状孔阵CCS普适性模型

Universal model of CCS for aperture arrays with multiple shapes based on two-stage neural network

摘要:

孔缝耦合截面(CCS)是评估孔缝穿透效应的重要参数，使用BP神经网络预测CCS，其预测速度远高于全波分析法，且精确度优于传统公式法。本文围绕能够适用于多种单元形状孔阵CCS的预测模型展开研究，提出3种用于预测孔阵CCS的神经网络模型，包括1种单级模型和2种双级模型。以正六边形孔阵为例对比3种模型的性能，结果表明引入先验信息最多的双级模型性能最优。该模型预测正六边形孔阵CCS的均方根误差为0.017 2，决定系数为0.999 1。将该模型进行迁移，可实现对圆形孔阵和方形孔阵CCS的预测，其样本的平均相对误差为1.94%。预测结果证实了该模型的精确性、高效性和普适性。

Abstract:

The Coupling Cross Section(CCS) of aperture is an important parameter to evaluate the effect of aperture penetration. Using BP neural network to predict CCS has a much higher prediction speed than full-wave analysis and better accuracy than traditional formula methods. This paper focuses on the prediction model which can be applied to multi-shape aperture array. Three neural network models are proposed to predict the CCS of aperture array, including one traditional single-stage model and two two-stage models. Taking the regular hexagonal aperture array as an example, the performance of the three models is compared. These results show that the double-level model with the most prior information performs the best. The Root Mean Square Error(RMSE) of the CCS prediction for the regular hexagonal aperture array by this model is 0.017 2, and the coefficient of determination(R) is 0.999 1. When this model is transferred, it can predict the CCS of circular and square aperture arrays, with an average relative error of 1.94% for the samples. The prediction results confirm the precision, efficiency, and universality of the model.