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

abstract

• 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.

authors

• Huang, Tingwen

published proceedings

• IEEE Trans Neural Netw Learn Syst

altmetric score

• 0.25

author list (cited authors)

• Wang, Z., Li, Q., Wu, H., Luo, B., & Huang, T.

citation count

• 8

complete list of authors

• Wang, Zi-Peng||Li, Qian-Qian||Wu, Huai-Ning||Luo, Biao||Huang, Tingwen

publication date

• October 2023

publisher

• Institute of Electrical and Electronics Engineers (IEEE)  Publisher

keywords

• Artificial Neural Networks
• Costs
• Couplings
• Deception Attacks
• Delays
• Directed Coupled Reaction-diffusion Neural Networks (crdnns)
• Pinning Spatiotemporal Sampled-data (sd) Control
• Sensors
• Spatiotemporal Phenomena
• Synchronization

PubMed Central ID

• 35171780

Digital Object Identifier (DOI)

• 10.1109/TNNLS.2022.3148184

start page

• 7967

end page

• 7977

volume

• 34

issue

• 10

URL

• http://dx.doi.org/10.1109/tnnls.2022.3148184