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Stabilization of stochastic delayed networks with Markovian switching via intermittent control: an averaging technique

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Abstract

This paper considers the stabilization of stochastic delayed networks with Markovian switching via aperiodically intermittent control (AIC). The concepts of average control ratio and average control period are proposed to characterize the distributions of control and rest intervals of AIC. It should be noted that the averaging technique used here is more general and less restrictive than the quasi-periodicity condition and minimum control ratio condition used in previous works. Then two kinds of stabilization criteria are obtained: (1) the upper bound of time delay should be less than the average control width; (2) the upper bound of time delay has no relationship with the average control width. Finally, the results are applied to studying the stabilization of coupled stochastic neural networks with Markovian switching via AIC. Numerical simulations are provided to show the effectiveness of obtained results.

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Acknowledgements

This work was supported by Shandong Province Natural Science Foundation (Nos. ZR2017MA008, ZR2017BA007 and ZR2018MA023); the Project of Shandong Province Higher Educational Science and Technology Program of China (Nos. J18KA218 and J16LI09), and Shandong Province Innovation Training Project for College Students (No. S201910429099).

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Authors and Affiliations

  1. Department of Mathematics, Qingdao University of Technology, Qingdao, 266520, People’s Republic of China

    Ying Guo

  2. Shenzhen Key Laboratory of Advanced Machine Learning and Application, College of Mathematics and Statistics, Shenzhen University, Shenzhen, 518060, People’s Republic of China

    Jiqiang Feng

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  1. Ying Guo
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Guo, Y., Feng, J. Stabilization of stochastic delayed networks with Markovian switching via intermittent control: an averaging technique. Neural Comput & Applic 34, 4487–4499 (2022). https://doi.org/10.1007/s00521-021-06603-5

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