Prediction of coupling section of circular aperture based on BP neural network

Zhu Lei; Liu Qiang; Zhao Xiang; Yan Liping; Zhou Haijing

doi:10.11884/HPLPB201931.190011

Volume 31 Issue 3

Mar. 2019

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Zhu Lei, Liu Qiang, Zhao Xiang, et al. Prediction of coupling section of circular aperture based on BP neural network[J]. High Power Laser and Particle Beams, 2019, 31: 033201. doi: 10.11884/HPLPB201931.190011

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Prediction of coupling section of circular aperture based on BP neural network

doi: 10.11884/HPLPB201931.190011

1.
School of Electronics and Information Engineering, Sichuan University, Chengdu 610064, China
2.
Beijing Institute of Applied Physics and Computational Mathematics, Beijing 100088, China

Received Date: 2019-01-04
Rev Recd Date: 2019-02-25
Publish Date: 2019-03-15

Abstract

Abstract

It is important to obtain the coupling section(CS) of aperture for analyzing aperture penetration/leakage of electromagnetic wave. However, there are no ready-made formulas for calculating CS of circular apertures in the resonance region. Therefore, a backpropagation(BP) neural network is applied to the fast acquisition of CS of circular apertures with electrical dimensions (ratio of radius and wavelength) of [0.08, 3]. Full-wave analysis software is used to calculate the CSs of circular apertures with different electrical dimensions on an infinitely large, perfectly conducting plate illuminated by plane-waves at different incident angles and polarization angles. By dividing the CS by the geometric area of the aperture, the normalized CS is obtained. And then, the full-wave analysis data are used as training data for a BP neural network model, including aperture electrical dimension, incident angle and polarization angle as input and normalized CS as output. Test results suggest that this model can quickly and accurately predict the normalized CSs of circular aperture with electrical dimensions [0.08, 3] under a plane-wave illumination at any incident angle and polarization angle.
- circular aperture,
- coupling section,
- full-wave analysis,
- normalized coupling section,
- neural network model

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References

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