On Phase Information for Deep Neural Networks to Solve Full-Wave Nonlinear Inverse Scattering Problems

Xiao Min Pan; Bo Yue Song; Di Wu; Guohua Wei; Xin Qing Sheng

doi:10.1109/LAWP.2021.3100135

On Phase Information for Deep Neural Networks to Solve Full-Wave Nonlinear Inverse Scattering Problems

Xiao Min Pan^*, Bo Yue Song, Di Wu, Guohua Wei, Xin Qing Sheng

^*此作品的通讯作者

Beijing Institute of Technology

科研成果: 期刊稿件 › 文章 › 同行评审

7 引用（Scopus）

摘要

The phase information's role in deep neural networks (DNNs) to solve the electromagnetic inverse scattering problems is investigated. The feedforward neutral network model with complex-valued (CV) data stream and the corresponding CV backpropagation training algorithm are proposed to realize CV convolutional neural networks. Numerical examples are carried out to demonstrate the phase information's role in DNNs in terms of generalization capability as well as the convergence speed in the training stage.

源语言	英语
页（从-至）	1903-1907
页数	5
期刊	IEEE Antennas and Wireless Propagation Letters
卷	20
期	10
DOI	https://doi.org/10.1109/LAWP.2021.3100135
出版状态	已出版 - 1 10月 2021

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Pan, X. M., Song, B. Y., Wu, D., Wei, G., & Sheng, X. Q. (2021). On Phase Information for Deep Neural Networks to Solve Full-Wave Nonlinear Inverse Scattering Problems. IEEE Antennas and Wireless Propagation Letters, 20(10), 1903-1907. https://doi.org/10.1109/LAWP.2021.3100135

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abstract = "The phase information's role in deep neural networks (DNNs) to solve the electromagnetic inverse scattering problems is investigated. The feedforward neutral network model with complex-valued (CV) data stream and the corresponding CV backpropagation training algorithm are proposed to realize CV convolutional neural networks. Numerical examples are carried out to demonstrate the phase information's role in DNNs in terms of generalization capability as well as the convergence speed in the training stage.",

keywords = "Complex-valued (CV), deep learning (DL), electromagnetic inverse scattering, nonlinear",

author = "Pan, {Xiao Min} and Song, {Bo Yue} and Di Wu and Guohua Wei and Sheng, {Xin Qing}",

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doi = "10.1109/LAWP.2021.3100135",

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AU - Pan, Xiao Min

AU - Song, Bo Yue

AU - Wu, Di

AU - Wei, Guohua

AU - Sheng, Xin Qing

PY - 2021/10/1

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N2 - The phase information's role in deep neural networks (DNNs) to solve the electromagnetic inverse scattering problems is investigated. The feedforward neutral network model with complex-valued (CV) data stream and the corresponding CV backpropagation training algorithm are proposed to realize CV convolutional neural networks. Numerical examples are carried out to demonstrate the phase information's role in DNNs in terms of generalization capability as well as the convergence speed in the training stage.

AB - The phase information's role in deep neural networks (DNNs) to solve the electromagnetic inverse scattering problems is investigated. The feedforward neutral network model with complex-valued (CV) data stream and the corresponding CV backpropagation training algorithm are proposed to realize CV convolutional neural networks. Numerical examples are carried out to demonstrate the phase information's role in DNNs in terms of generalization capability as well as the convergence speed in the training stage.

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KW - electromagnetic inverse scattering

KW - nonlinear

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ER -

On Phase Information for Deep Neural Networks to Solve Full-Wave Nonlinear Inverse Scattering Problems

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