• High Power Laser and Particle Beams
  • Vol. 34, Issue 5, 053001 (2022)
Zhibin He, Liping Yan, and Xiang Zhao*
Author Affiliations
  • College of Electronic and Information Engineering, Sichuan University, Chengdu 610065, China
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    DOI: 10.11884/HPLPB202234.210566 Cite this Article
    Zhibin He, Liping Yan, Xiang Zhao. Prediction of coupling cross section of hexagonal aperture array based on BP neural network[J]. High Power Laser and Particle Beams, 2022, 34(5): 053001 Copy Citation Text show less
    Numerical experimental system model
    Fig. 1. Numerical experimental system model
    Effect of row/column number on the normalized CCS
    Fig. 2. Effect of row/column number on the normalized CCS
    Effect of polarization angle on the normalized CCS
    Fig. 3. Effect of polarization angle on the normalized CCS
    Absolute difference of normalized CCS between other polarization angles and 0°
    Fig. 4. Absolute difference of normalized CCS between other polarization angles and 0°
    Variation of normalized CCS with α and while l/λ is 0.295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
    Fig. 5. Variation of normalized CCS with α and while l/λ is 0.295 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
    Histogram of relative error distribution of neural network fitting results
    Fig. 6. Histogram of relative error distribution of neural network fitting results
    Relation between and error of neural network fitting result电尺寸与神经网络拟合结果误差关系图
    Fig. 7. Relation between and error of neural network fitting result 电尺寸与神经网络拟合结果误差关系图
    Histogram of relative error distribution of neural network prediction results
    Fig. 8. Histogram of relative error distribution of neural network prediction results
    Comparison of predicted values of neural network and true values
    Fig. 9. Comparison of predicted values of neural network and true values
    Experimental system in anechoic chamber
    Fig. 10. Experimental system in anechoic chamber
    Comparison of predicted values of neural network and experimental result
    Fig. 11. Comparison of predicted values of neural network and experimental result
    $ \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
    Table 1. Range of input parameters
    Zhibin He, Liping Yan, Xiang Zhao. Prediction of coupling cross section of hexagonal aperture array based on BP neural network[J]. High Power Laser and Particle Beams, 2022, 34(5): 053001
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