Efficient Broadband Monostatic RCS Computation of Morphing S-Shape Cavity Using Artificial Neural Networks-Reference-Cited by-同舟云学术

Efficient Broadband Monostatic RCS Computation of Morphing S-Shape Cavity Using Artificial Neural Networks

Published:2023-02 Issue:2 Volume:22 Page:263-267
ISSN:1536-1225
Container-title:IEEE Antennas and Wireless Propagation Letters
language:
Short-container-title:Antennas Wirel. Propag. Lett.

Author:

Weng Rui¹,Sun Dingyi¹,Yang Wu¹^ORCID,Chen Xinlei²^ORCID,Lu Weibing¹^ORCID

Affiliation:

1. State Key Laboratory of Millimeter waves, School of Information Science and Engineering, Southeast University, Nanjing, China

2. Key laboratory of Radar Imaging and Microwave Photonics, Ministry of Education, College of Electronic and Information Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, China

Funder

China National Funds for Distinguished Young Scientists

Key Program of National Natural Science Foundation of China

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/ielx7/7727/10035676/09899702.pdf?arnumber=9899702

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4. Analysis of Partial Geometry Modification Problems Using the Partitioned-Inverse Formula and Sherman–Morrison–Woodbury Formula-Based Method

5. Deep sparse rectifier neural networks;glorot;Proc 14th Int Conf Artif Intell Statist,2011

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