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PIER PIER B PIER C PIER M PIER Letters PIER Photonics
2020-08-08
A Spatial SEM-Based Shallow Neural Network for Electromagnetic Inverse Source Modeling
By
Abdelelah Alzahed,

    Dr. Abdelelah Alzahed

    ECE Department

    Royal Militay College of Canada

    Canada

    Homepage

Said Mikki,

    Prof. Said Mikki

    Zhejiang University/University of Illinois at Urbana-Champaign (ZJU-UIUC) Institute

    China

    Homepage

Yahia M. Antar

    Professor Yahia M. Antar

    Dept Elect Engn

    Royal Military College of Canada

    Canada

    Homepage

Progress In Electromagnetics Research M, Vol. 95, 53-61, 2020
doi:10.2528/PIERM20040101 | Google Scholar

Abstract
We derive and verify a new type of low-complexity neural networks using the recently introduced spatial singularity expansion method (S-SEM). The neural network consists of a single layer (Shallow Learning approach to machine learning) but with its activation function replaced by specialized S-SEM radiation mode functions derived by electromagnetic theory. The proposed neural network can be trained by measured near- or far-field data, e.g., RCS, probe-measured fields, array manifold samples, in order to reproduce the unknown source current on the radiating structure. We apply the method to wire structures and show that the various spatial resonances of the radiating current can be very efficiently predicted by the S-SEM-based neural network. Convergence results are compared with Genetic Algorithms and are found to be considerably superior in speed and accuracy.
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Citation
Abdelelah Alzahed, Said Mikki, and Yahia M. Antar, "A Spatial SEM-Based Shallow Neural Network for Electromagnetic Inverse Source Modeling," Progress In Electromagnetics Research M, Vol. 95, 53-61, 2020.
doi:10.2528/PIERM20040101
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