Event-based sliding-mode synchronization of delayed memristive neural networks via continuous/periodic sampling algorithm

• Published in: Applied mathematics and computation Vol. 383; p. 125379
• Main Authors: Wang, Yuxiao, Cao, Yuting, Guo, Zhenyuan, Huang, Tingwen, Wen, Shiping
• Format: Journal Article
• Language: English
• Published: Elsevier Inc 15.10.2020
• Subjects: Event-triggering; Memristive neural network; Periodic sampling; Sliding mode control; Memristive neural network; Periodic sampling; Event-triggering; Sliding mode control
• ISSN: 0096-3003; 1873-5649
• DOI: 10.1016/j.amc.2020.125379

•This paper investigates the problem of event-based sliding-mode synchronization of memristive neural networks with delay through continuous/periodic sampling algorithm.•Memristive neural networks are converted into the form of general neural networks by nonsmooth analysis.•The controller is designed on the sliding manifold selected and the trajectory of the system with this controller are analyzed in detail.•Based on the continuous sampling, this paper further draws new results with the periodic sampling rule.•Finally, some numerical examples are given to verify the correctness of the theoretical results. This paper investigates the problem of event-based sliding-mode synchronization of memristive neural networks with delay through continuous/periodic sampling algorithm. Memristive neural networks are converted into the form of general neural networks by nonsmooth analysis. Then the controller is designed on the sliding surface selected and the trajectory of the system with this controller are analyzed in detail. Based on the continuous sampling, this paper further draws new results with the periodic sampling rule. Finally, some numerical examples are given to verify the correctness of the theoretical results.