Rapid Subentire-Domain Basis Functions Method Based on Adaptive Artificial Neural Networks-Reference-Cited by-同舟云学术

Rapid Subentire-Domain Basis Functions Method Based on Adaptive Artificial Neural Networks

Published:2022-07 Issue:7 Volume:70 Page:5156-5164
ISSN:0018-926X
Container-title:IEEE Transactions on Antennas and Propagation
language:
Short-container-title:IEEE Trans. Antennas Propagat.

Author:

Xiang Wei¹,Zhang Zhe¹,Zheng Wenming²^ORCID,Li Jiaqi³,Yang Wu¹^ORCID,Lu Weibing¹^ORCID

Affiliation:

1. State Key Laboratory of Millimeter Waves, Center for Flexible RF Technology, School of Information Science and Engineering, Southeast University, Nanjing, China

2. Key Laboratory of Child Development and Learning Science, Ministry of Education, and the School of Biological Science and Medical Engineering, Southeast University, Nanjing, China

3. School of Physics, Southeast University, Nanjing, China

Funder

China National Funds for Distinguished Young Scientists

Project for Jiangsu Specially-Appointed Professor

Fundamental Research Funds for the Central Universities

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering

Link

http://xplorestaging.ieee.org/ielx7/8/9843191/09696216.pdf?arnumber=9696216

Reference25 articles.

1. An Iteration-Free MoM Approach Based on Excitation Independent Characteristic Basis Functions for Solving Large Multiscale Electromagnetic Scattering Problems

2. Synthetic function analysis of large printed structures: the solution space sampling approach

3. A REVIEW OF DEEP LEARNING APPROACHES FOR INVERSE SCATTERING PROBLEMS (INVITED REVIEW)

4. Learning-by-examples techniques as applied to electromagnetics

5. Real-Time NDT-NDE Through an Innovative Adaptive Partial Least Squares SVR Inversion Approach

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