Zonotope-Based Distributed Set-Membership Fusion Estimation for Artificial Neural Networks Under the Dynamic Event-Triggered Mechanism-Reference-Cited by-同舟云学术

Zonotope-Based Distributed Set-Membership Fusion Estimation for Artificial Neural Networks Under the Dynamic Event-Triggered Mechanism

Published:2025-01 Issue:1 Volume:36 Page:1637-1650
ISSN:2162-237X
Container-title:IEEE Transactions on Neural Networks and Learning Systems
language:
Short-container-title:IEEE Trans. Neural Netw. Learning Syst.

Author:

Zhao Zhongyi¹^ORCID,Wang Zidong²^ORCID,Zou Lei³^ORCID,Liu Hongjian⁴^ORCID,Sheng Weiguo⁵^ORCID

Affiliation:

1. School of Mathematics, Southeast University, Nanjing, China

2. Department of Computer Science, Brunel University London, Uxbridge, U.K

3. College of Information Science and Technology, Donghua University, Shanghai, China

4. Key Laboratory of Advanced Perception and Intelligent Control of High-end Equipment, Ministry of Education, Anhui Polytechnic University, Wuhu, China

5. School of Information Science and Engineering, Hangzhou Normal University, Hangzhou, China

Funder

National Natural Science Foundation of China

Shanghai Pujiang Program of China

“Pioneer” and “Leading Goose” Research and Development Program of Zhejiang Province of China

AHPU Youth Top-Notch Talent Support Program of China

Foundation of Anhui Future Technology Research Institute of China

AHPU High-End Equipment Intelligent Control Innovation Team of China

Royal Society of the U.K.

Alexander von Humboldt Foundation of Germany

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/5962385/10832116/10318827.pdf?arnumber=10318827

Reference50 articles.

1. Guaranteed state estimation by zonotopes

2. Distributed fusion filters from uncertain measured outputs in sensor networks with random packet losses

3. Centralized, distributed and sequential fusion estimation from uncertain outputs with correlation between sensor noises and signal

4. Fractional-order convolutional neural networks with population extremal optimization

5. Improved Results on $$L_2-L_\infty $$ State Estimation for Neural Networks with Time-varying Delay

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