Design of Reconfigurable Periodic Structures Based on Machine Learning-Reference-Cited by-同舟云学术

Design of Reconfigurable Periodic Structures Based on Machine Learning

Published:2023-08 Issue:8 Volume:71 Page:3341-3351
ISSN:0018-9480
Container-title:IEEE Transactions on Microwave Theory and Techniques
language:
Short-container-title:IEEE Trans. Microwave Theory Techn.

Author:

Fang Xiaoxing¹^ORCID,Li Huangyan²^ORCID,Cao Qunsheng³^ORCID

Affiliation:

1. Research Center of Applied Electromagnetics, School of Electronic and Information Engineering, Nanjing University of Information Science and Technology, Nanjing, China

2. School of Electronic and Optical Engineering, Nanjing University of Science and Technology, Nanjing, China

3. College of Electronic and Information Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, China

Funder

National Nature Science Foundation of China

Startup Foundation for Introducing Talent of the Nanjing University of Information Science and Technology

Key Laboratory of Radar Imaging and Microwave Photonics, Nanjing University of Aeronautics and Astronautics, Ministry of Education

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Condensed Matter Physics,Radiation

Link

http://xplorestaging.ieee.org/ielx7/22/10213372/10012120.pdf?arnumber=10012120

Reference32 articles.

1. Machine learning: Trends, perspectives, and prospects

2. A Wide-Angle Scanning and Low Sidelobe Level Microstrip Phased Array Based on Genetic Algorithm Optimization

3. Efficient Prediction of the EM Response of Reflectarray Antenna Elements by an Advanced Statistical Learning Method

4. ANN Characterization of Multi-Layer Reflectarray Elements for Contoured-Beam Space Antennas in the Ku-Band

5. Deep-learning-enabled self-adaptive microwave cloak without human intervention

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