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

• Published in: IEEE transaction on neural networks and learning systems Vol. 34; no. 10; pp. 7967 - 7977
• Main Authors: Wang, Zi-Peng, Li, Qian-Qian, Wu, Huai-Ning, Luo, Biao, Huang, Tingwen
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.10.2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• 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
• ISSN: 2162-237X; 2162-2388
• DOI: 10.1109/TNNLS.2022.3148184

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.