An Uncertainty Analysis Method of Wave Propagation in Complex Media Based on Artificial Neural Network

Xi CHENG; Zhiyong ZHANG

doi:10.11999/JEIT200755

Volume 43 Issue 12

Dec. 2021

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Article Navigation > Journal of Electronics & Information Technology > 2021 > 43(12): 3662-3670

Xi CHENG, Zhiyong ZHANG. An Uncertainty Analysis Method of Wave Propagation in Complex Media Based on Artificial Neural Network[J]. Journal of Electronics & Information Technology, 2021, 43(12): 3662-3670. doi: 10.11999/JEIT200755

Citation:

Xi CHENG, Zhiyong ZHANG. An Uncertainty Analysis Method of Wave Propagation in Complex Media Based on Artificial Neural Network[J]. Journal of Electronics & Information Technology, 2021, 43(12): 3662-3670. doi: 10.11999/JEIT200755

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An Uncertainty Analysis Method of Wave Propagation in Complex Media Based on Artificial Neural Network

doi: 10.11999/JEIT200755

Xi CHENG,
Zhiyong ZHANG^,

Institute of Computer and Information Engineering, XinJiang Agricultural University, Urumqi 830052, China

Funds: The National Natural Science Foundation of China (61701427)

Received Date: 2020-08-26
Rev Recd Date: 2021-09-11

Available Online: 2021-10-28

Publish Date: 2021-12-21

Abstract

Abstract

Soil materials can exhibit strongly dispersive properties in the operating frequency range of a physical system, and the uncertain parameters of the dispersive materials introduce uncertainties in the simulation result of propagating waves. It is essential to quantify the uncertainty in the simulation result when the acceptability of these calculation results is considered. To avoid performing thousands of full-wave simulations, an efficient surrogate model based on ANN (Artificial Neural Network) is proposed, to imitate the concerned Ground Penetrating Radar (GPR) calculation. Meanwhile, the process of constructing the surrogate model and the strategy to overcome the overfitting problem are presented in details. As a surrogate model for full-wave simulation of ground penetrating radar, it can predict the simulation result, and then obtain the statistical information of the simulation result, such as mean value and standard deviation. After comparison, under the same conditions that the same numerical model, the number of uncertain input parameters are same, and the variation in the parameter is 10%, the statistical properties of the prediction results obtained by the proposed method are in good agreement with the results obtained by performing a thousand full-wave simulations. It also significantly reduces the amount of calculations, and the calculation time efficiency is increased by 79.82%.
- Ground Penetrating Radar (GPR),
- Uncertainty analysis model,
- Artificial Neural Network ( ANN)

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References(19)

References

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