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基于BP神经网络的六边形孔阵耦合截面的预测

贺智彬 闫丽萍 赵翔

贺智彬, 闫丽萍, 赵翔. 基于BP神经网络的六边形孔阵耦合截面的预测[J]. 强激光与粒子束, 2022, 34: 053001. doi: 10.11884/HPLPB202234.210566
引用本文: 贺智彬, 闫丽萍, 赵翔. 基于BP神经网络的六边形孔阵耦合截面的预测[J]. 强激光与粒子束, 2022, 34: 053001. doi: 10.11884/HPLPB202234.210566
He Zhibin, Yan Liping, Zhao Xiang. Prediction of coupling cross section of hexagonal aperture array based on BP neural network[J]. High Power Laser and Particle Beams, 2022, 34: 053001. doi: 10.11884/HPLPB202234.210566
Citation: He Zhibin, Yan Liping, Zhao Xiang. Prediction of coupling cross section of hexagonal aperture array based on BP neural network[J]. High Power Laser and Particle Beams, 2022, 34: 053001. doi: 10.11884/HPLPB202234.210566

基于BP神经网络的六边形孔阵耦合截面的预测

doi: 10.11884/HPLPB202234.210566
基金项目: 国家自然科学基金面上项目(61877041)
详细信息
    作者简介:

    贺智彬,hzb2500199128@qq.com

    通讯作者:

    赵 翔,zhaoxiang@scu.edu.cn

  • 中图分类号: TN911

Prediction of coupling cross section of hexagonal aperture array based on BP neural network

  • 摘要: 孔缝耦合截面作为度量电磁能量经孔缝泄漏强弱的重要参数,一直没有一个普适快速且精度较高的获取方法。针对六边形孔阵归一化耦合截面的获取问题,分析了垂直入射条件下各因素对六边形孔阵耦合截面的影响,选择合适的参数并使用全波分析法共获取13820组耦合截面数据。对部分输入参数进行预处理后输入神经网络进行训练,构建了一个以孔单元电尺寸、行/列数、行/列间距电尺寸、孔壁厚度电尺寸、入射波极化角度等7个参数为输入,归一化耦合截面为输出的BP神经网络模型。该模型在预测电尺寸为[0.1,1.2]时的归一化耦合截面平均相对误差为3.8%。选取未出现在神经网络训练集与测试集中的输入参数,比较全波分析法计算值和神经网络预测值共480组数据,其平均相对误差为7.27%。最后通过实验测量,进一步验证了该模型的普适性和有效性。
  • 图  1  数值实验系统模型

    Figure  1.  Numerical experimental system model

    图  2  行列数对孔缝耦合截面的影响

    Figure  2.  Effect of row/column number on the normalized CCS

    图  3  极化角度对孔阵归一化耦合截面的影响

    Figure  3.  Effect of polarization angle on the normalized CCS

    图  4  其他极化角度与0°归一化耦合截面绝对差值

    Figure  4.  Absolute difference of normalized CCS between other polarization angles and 0°

    图  5  电尺寸为0.295时孔阵归一化耦合截面随极化角度和孔间距电尺寸的变化

    Figure  5.  Variation of normalized CCS with α and $ {d}_{x}/\lambda $ while l/λ is 0.295

    图  6  神经网络拟合结果相对误差分布直方图

    Figure  6.  Histogram of relative error distribution of neural network fitting results

    图  7  电尺寸与神经网络拟合结果误差关系图

    Figure  7.  Relation between $ l/\lambda $ and error of neural network fitting result

    图  8  神经网络预测结果相对误差分布直方图

    Figure  8.  Histogram of relative error distribution of neural network prediction results

    图  9  神经网络预测值与真实值比较图

    Figure  9.  Comparison of predicted values of neural network and true values

    图  10  微波暗室实验图

    Figure  10.  Experimental system in anechoic chamber

    图  11  实验测量结果与神经网络预测结果对比

    Figure  11.  Comparison of predicted values of neural network and experimental result

    表  1  输入参数变化范围

    Table  1.   Range of input parameters

    $ \mathit{l}/\mathit{\lambda } $$ {\mathit{n}}_{\mathit{x}} $$ {\mathit{n}}_{\mathit{y}} $$ {\mathit{d}}_{\mathit{x}}/\mathit{\lambda } $$ {\mathit{d}}_{\mathit{y}}/\mathit{\lambda } $$ \mathit{\alpha } $$ \mathit{h}/\mathit{\lambda } $
    0.05~1.201~81~80.025~3.0000.025~3.0000~$ \mathrm{\pi }/2 $0.0005~3.0000
    下载: 导出CSV
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出版历程
  • 收稿日期:  2021-12-20
  • 修回日期:  2022-03-17
  • 录用日期:  2022-03-18
  • 网络出版日期:  2022-03-23
  • 刊出日期:  2022-05-15

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