Synchronization of Coupled Neural Networks With Constant Time-Delay Using Sampled-Data Information

In this article, a synchronization control method is studied for coupled neural networks (CNNs) with constant time delay using sampled-data information. A distributed control protocol relying on the sampled-data information of neighboring nodes is proposed. Lyapunov functional is constructed to anal...

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Bibliographic Details
Published in:IEEE transactions on cybernetics 2024-08, Vol.54 (8), p.4702-4711
Main Authors: Liu, Xiang, Liao, Siqin, Wu, Zheng-Guang, Wu, Yuanqing
Format: Article
Language:English
Subjects:
Biological neural networks
Coupled neural networks (CNNs)
Delay effects
Delays
Eigenvalues and eigenfunctions
Linear matrix inequalities
Protocols
sampled-data information
Synchronization
time delay
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Summary:In this article, a synchronization control method is studied for coupled neural networks (CNNs) with constant time delay using sampled-data information. A distributed control protocol relying on the sampled-data information of neighboring nodes is proposed. Lyapunov functional is constructed to analyze the synchronization of CNNs with constant time delay. Using Park's integral inequality and improved free-weight matrix integral inequality, sufficient conditions are provided for CNNs to achieve synchronization with less conservatism. In addition, the maximum sampling interval is determined by transforming the sufficient conditions into an optimization problem, and an aperiodic sampling control technique is implemented to reduce the communication energy load. Finally, numerical simulations are provided to demonstrate that the proposed method is capable of achieving synchronization.
ISSN:2168-2267
2168-2275
2168-2275
DOI:10.1109/TCYB.2023.3318987