Chance-Constrained $H_{∞}$ State Estimation for Recursive Neural Networks Under Deception Attacks and Energy Constraints: The Finite-Horizon Case-Reference-Cited by-同舟云学术

Chance-Constrained $H_{∞}$ State Estimation for Recursive Neural Networks Under Deception Attacks and Energy Constraints: The Finite-Horizon Case

Published:2022 Issue: Volume: Page:1-12
ISSN:2162-237X
Container-title:IEEE Transactions on Neural Networks and Learning Systems
language:
Short-container-title:IEEE Trans. Neural Netw. Learning Syst.

Author:

Qu Fanrong¹,Tian Engang²^ORCID,Zhao Xia³

Affiliation:

1. College of Science, University of Shanghai for Science and Technology, Shanghai 200093, China.

2. School of Optical Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China (e-mail: tianengang@163.com)

3. College of Science, University of Shanghai for Science and Technology, Shanghai 200093, China, and also with the School of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China.

Funder

National Natural Science Foundation of China

Natural Science Foundation of Shanghai

Program for Professor of Special Appointment (Eastern Scholar) at Shanghai Institutions of Higher Learning

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Artificial Intelligence,Computer Networks and Communications,Computer Science Applications,Software

Link

http://xplorestaging.ieee.org/ielx7/5962385/6104215/09674200.pdf?arnumber=9674200

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