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

•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 designe...

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Vydané v:Applied mathematics and computation Ročník 383; s. 125379
Hlavní autori: Wang, Yuxiao, Cao, Yuting, Guo, Zhenyuan, Huang, Tingwen, Wen, Shiping
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Elsevier Inc 15.10.2020
Predmet:
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
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Popis
Shrnutí:•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.
ISSN:0096-3003
1873-5649
DOI:10.1016/j.amc.2020.125379