TY - GEN
T1 - Fast Prediction of Electromagnetic Scattering Fields Based on Machine Learning and PSO Algorithm
AU - Zhang, Zhourui
AU - He, Mang
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - In this paper, a method based on support vector regression (SVR) using radial basis function (RBF) and the particle swarm optimization (PSO) algorithm is proposed to accurately predict electromagnetic (EM) scattering fields versus any incident angle at the specific frequency we are interested in. Compared with the simulation results by FEKO using the method of moment (MoM), this method can accurately predict the monostatic RCS with the root-mean-square error (RMSE) less than -9 dBsm.
AB - In this paper, a method based on support vector regression (SVR) using radial basis function (RBF) and the particle swarm optimization (PSO) algorithm is proposed to accurately predict electromagnetic (EM) scattering fields versus any incident angle at the specific frequency we are interested in. Compared with the simulation results by FEKO using the method of moment (MoM), this method can accurately predict the monostatic RCS with the root-mean-square error (RMSE) less than -9 dBsm.
KW - Radar cross-section (RCS)
KW - electromagnetic scattering fields
KW - machine learning (ML)
KW - particle swarm optimization (PSO)
UR - http://www.scopus.com/inward/record.url?scp=85151992921&partnerID=8YFLogxK
U2 - 10.1109/APCAP56600.2022.10069242
DO - 10.1109/APCAP56600.2022.10069242
M3 - Conference contribution
AN - SCOPUS:85151992921
T3 - 2022 IEEE 10th Asia-Pacific Conference on Antennas and Propagation, APCAP 2022 - Proceedings
BT - 2022 IEEE 10th Asia-Pacific Conference on Antennas and Propagation, APCAP 2022 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 10th IEEE Asia-Pacific Conference on Antennas and Propagation, APCAP 2022
Y2 - 4 November 2022 through 7 November 2022
ER -