Pinning Spatiotemporal Sampled-Data Synchronization of Coupled Reaction-Diffusion Neural Networks Under Deception Attacks

In this article, we investigate the pinning spatiotemporal sampled-data (SD) synchronization of coupled reaction-diffusion neural networks (CRDNNs), which are directed networks with SD in time and space communications under random deception attacks. In order to handle with the random deception attac...

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Bibliographic Details
Published in:IEEE transaction on neural networks and learning systems 2023-10, Vol.34 (10), p.7967-7977
Main Authors: Wang, Zi-Peng, Li, Qian-Qian, Wu, Huai-Ning, Luo, Biao, Huang, Tingwen
Format: Article
Language:English
Subjects:
Artificial neural networks
Costs
Couplings
Deception attacks
Delays
directed coupled reaction–diffusion neural networks (CRDNNs)
Linear matrix inequalities
Mathematical analysis
Neural networks
Pinning
pinning spatiotemporal sampled-data (SD) control
Sensors
Spatiotemporal data
Spatiotemporal phenomena
Stochasticity
Synchronism
Synchronization
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Summary:In this article, we investigate the pinning spatiotemporal sampled-data (SD) synchronization of coupled reaction-diffusion neural networks (CRDNNs), which are directed networks with SD in time and space communications under random deception attacks. In order to handle with the random deception attacks, we establish a directed CRDNN model, which respects the impacts of variable sampling and random deception attacks within a unified framework. Through the designed pinning spatiotemporal SD controller, sufficient conditions are obtained by linear matrix inequalities (LMIs) that guarantee the mean square exponential stability of the synchronization error system (SES) derived by utilizing inequality techniques, the stochastic analysis technique, and Lyapunov-Krasovskii functional (LKF). Finally, a numerical example is utilized to support the presented pinning spatiotemporal SD synchronization method.
ISSN:2162-237X
2162-2388
DOI:10.1109/TNNLS.2022.3148184