@inproceedings{373eebf087bb4efbbc62ae1d812e90ed,
title = "Using Machine Learning to Represent Electromagnetic Characteristics of Arbitrarily-shaped Targets",
abstract = "A general data sparse representation of electromagnetic characteristics of an arbitrarily-shaped target is developed by using the machine learning model. The data sparse representation of the electromagnetic response is firstly figured out by the skeletonization technique. The machine learning approach is then employed to construct a general and flexible model which can capture the electromagnetic characteristics of the target of interest. Numerical experiments are conducted to validate the performance of the model.",
keywords = "Machine Learning, Method of Moments(MoM), artificial neural network(ANN), data sparse representation",
author = "Pan, {Xiao Min} and Song, {Bo Yue} and Huang, {Si Lu} and Sheng, {Xin Qing}",
note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 5th IEEE International Conference on Computational Electromagnetics, ICCEM 2019 ; Conference date: 20-03-2019 Through 22-03-2019",
year = "2019",
month = mar,
doi = "10.1109/COMPEM.2019.8778911",
language = "English",
series = "2019 IEEE International Conference on Computational Electromagnetics, ICCEM 2019 - Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2019 IEEE International Conference on Computational Electromagnetics, ICCEM 2019 - Proceedings",
address = "United States",
}