Abstract
In this paper, a novel translator calculation method for the multilevel fast multipole algorithm (MLFMA) is proposed based on a machine learning approach. The generalized regression neural networks are introduced to fit the translation function of MLFMA during the procedure of eletromagnetic scattering analysis. Compared to the traditional method, the new method inherits advantages of the generalized regression neural networks (GRNN) and can approximate the translator with high accuracy simultaneously. As an example, a two-level 2D MLFMA for a perfect electrically conductor (PEC) is finally implemented and the translators are reconstructed by using the proposed model. The numerical results validate the effectiveness of the proposed method.
Recommended Citation
J. J. Sun et al., "Machine Learning Based Multilevel Fast Multipole Algorithm," 2018 IEEE Antennas and Propagation Society International Symposium and USNC/URSI National Radio Science Meeting, APSURSI 2018 - Proceedings, pp. 2311 - 2312, article no. 8609019, Institute of Electrical and Electronics Engineers, Jan 2018.
The definitive version is available at https://doi.org/10.1109/APUSNCURSINRSM.2018.8609019
Department(s)
Electrical and Computer Engineering
International Standard Book Number (ISBN)
978-153867102-3
Document Type
Article - Conference proceedings
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2024 Institute of Electrical and Electronics Engineers, All rights reserved.
Publication Date
01 Jan 2018
Comments
National Natural Science Foundation of China, Grant FA2386-17-10010