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

He Zhibin; Yan Liping; Zhao Xiang

doi:10.11884/HPLPB202234.210566

Volume 34 Issue 5

Apr. 2022

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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

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

doi: 10.11884/HPLPB202234.210566

College of Electronic and Information Engineering, Sichuan University, Chengdu 610065, China

Received Date: 2021-12-20
Accepted Date: 2022-03-18
Rev Recd Date: 2022-03-17

Available Online: 2022-03-23

Publish Date: 2022-05-15

Abstract

Abstract

As an important parameter to measure the leakage of electromagnetic energy through apertures, there has not been a universal, fast and high precision method to obtain the coupling cross section (CCS). For obtaining the hexagonal aperture array normalized CCS, we analyze the influence of various factors on it under the condition of vertical incidence. A total of 13820 sets of CCS data are obtained by selecting appropriate parameters and using full-wave analysis method. After some input parameters are preprocessed and the neural network is trained, a BP neural network model has been constructed with seven parameters including the electrical dimension of the aperture unit, row/column number, the electrical dimension of the row/column distance, the electrical dimension of the aperture wall thickness and polarization angle of incident wave as the input and the normalized CCS as the output. The model has an average relative error of 3.8% when the predicted normalized CCS of the hexagonal aperture array has the electrical dimensions [0.1, 1.2]. A total of 480 CCSs with input parameters not appearing in both the training set and the test set are predicted by the neural network and compared with the full-wave analysis results, and the average relative error is 7.27%. Finally, the universality and effectiveness of the model are validated further by experimental measurement.
- coupling cross section,
- hexagonal aperture array,
- neural network,
- full wave analysis method,
- prediction accuracy

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References(17)

References

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