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Synchronization of memristive neural networks with mixed delays via quantized intermittent control

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  • Feng, Yuming
  • Yang, Xinsong
  • Song, Qiang
  • Cao, Jinde

Abstract

It is well known that how to deal with the effect of time delay and how to determine the control and rest widths are the main difficulties for intermittent control. This paper considers asymptotic synchronization of drive-response memristive neural networks (MNNs) with bounded time-varying discrete delay and unbounded distributed delay (mixed delays), which extends existing intermittent control techniques and reveals new relationship between control width and rest width. A quantized intermittent control (QIC) is designed to save both channel resources and control cost and reduce both the amount of transmitted information and channel blocking. Based on weighted double-integral inequalities, novel Lyapunov–Krasovskii functionals with negative terms are designed, which reduce the conservativeness of obtained results greatly. Sufficient conditions in terms of linear matrix inequalities (LMIs) are obtained to ensure the asymptotic synchronization. The control gains can also be designed by solving the LMIs. It is shown that the QIC can be neither periodic nor proportional between control width and rest width. Moreover, the relationships between control width, rest width, and convergence rate are explicitly given. Finally, numerical simulations are provided to illustrate the effectiveness of the theoretical analysis.

Suggested Citation

  • Feng, Yuming & Yang, Xinsong & Song, Qiang & Cao, Jinde, 2018. "Synchronization of memristive neural networks with mixed delays via quantized intermittent control," Applied Mathematics and Computation, Elsevier, vol. 339(C), pages 874-887.
    • Handle: RePEc:eee:apmaco:v:339:y:2018:i:c:p:874-887
    DOI: 10.1016/j.amc.2018.08.009
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    Cited by:

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    6. Ruofeng Rao & Jialin Huang & Xinsong Yang, 2021. "Global Stabilization of a Single-Species Ecosystem with Markovian Jumping under Neumann Boundary Value via Laplacian Semigroup," Mathematics, MDPI, vol. 9(19), pages 1-11, October.
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    8. Wang, Shengbo & Cao, Yanyi & Huang, Tingwen & Wen, Shiping, 2019. "Passivity and passification of memristive neural networks with leakage term and time-varying delays," Applied Mathematics and Computation, Elsevier, vol. 361(C), pages 294-310.
    9. Gao, Panqing & Ye, Renyu & Zhang, Hai & Stamova, Ivanka & Cao, Jinde, 2024. "Asymptotic stability and quantitative synchronization of fractional competitive neural networks with multiple restrictions," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 217(C), pages 338-353.
    10. Li, Zhao-Yan & Shang, Shengnan & Lam, James, 2019. "On stability of neutral-type linear stochastic time-delay systems with three different delays," Applied Mathematics and Computation, Elsevier, vol. 360(C), pages 147-166.
    11. Keke Wu & Babatunde Oluwaseun Onasanya & Longzhou Cao & Yuming Feng, 2023. "Impulsive Control of Some Types of Nonlinear Systems Using a Set of Uncertain Control Matrices," Mathematics, MDPI, vol. 11(2), pages 1-12, January.
    12. Hong, Yaxian & Bin, Honghua & Huang, Zhenkun, 2019. "Synchronization of state-switching hopfield-type neural networks: A quantized level set approach," Chaos, Solitons & Fractals, Elsevier, vol. 129(C), pages 16-24.
    13. Zhou, Chao & Wang, Chunhua & Yao, Wei & Lin, Hairong, 2022. "Observer-based synchronization of memristive neural networks under DoS attacks and actuator saturation and its application to image encryption," Applied Mathematics and Computation, Elsevier, vol. 425(C).
    14. Yan, Lisha & Wang, Zhen & Zhang, Mingguang & Fan, Yingjie, 2023. "Sampled-data control for mean-square exponential stabilization of memristive neural networks under deception attacks," Chaos, Solitons & Fractals, Elsevier, vol. 174(C).

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