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Exponential Synchronization of Stochastic Time-delayed Memristor-based Neural Networks via Pinning Impulsive Control

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Abstract

This paper investigates the exponential synchronization of stochastic time-delayed memristor-based neural networks(MBNNs) with using pinning impulsive control. Different from the traditional impulsive control schemes, a hybrid pinning impulsive control scheme is presented, and some sufficient conditions for exponential synchronization of system are established. Moreover, on the basis of the obtained results, the problem of delayed impulsive stabilization of stochastic time-delayed MBNN is studied. At last, an example is provided to demonstrate the validity of the obtained results.

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Correspondence to Pei Cheng.

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The authors declare that there is no competing financial interest or personal relationship that could have appeared to influence the work reported in this paper.

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This research were supported by the National Natural Science Foundation of China (11771001), the Key Natural Science Research Project of Universities of Anhui Province, China (2022AH050108, 22020721025).

Yao Cui received her M.S. degree from the School of Mathematical Sciences at Anhui University in 2021. She is currently pursuing a D.S. degree at Anhui University. Her research interests include impulsive control, stochastic system, and network systems.

Pei Cheng received her B.S. degree in applied mathematics from Hubei University of Science and Technology, China, in 2006 and a Ph.D. degree in system engineering from South China University of Technology, China, in 2011. She is presently a professor at the School of Mathematical Sciences, Anhui University, China. From September 2016 to September 2017, she was a visiting scholar at the Department of Mathematics, Wayne State University, USA. Her current research interests include hybrid stochastic systems, impulsive systems, and impulsive control.

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Cui, Y., Cheng, P. Exponential Synchronization of Stochastic Time-delayed Memristor-based Neural Networks via Pinning Impulsive Control. Int. J. Control Autom. Syst. (2024). https://doi.org/10.1007/s12555-022-1090-8

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