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Projective synchronization of memristive multidirectional associative memory neural networks via self-triggered impulsive control and its application to image protection

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  • Wang, Weiping
  • Sun, Yue
  • Yuan, Manman
  • Wang, Zhen
  • Cheng, Jun
  • Fan, Denggui
  • Kurths, Jürgen
  • Luo, Xiong
  • Wang, Chunyang

Abstract

This paper presents a new synchronization criterion with a hybrid control approach for multidirectional associative memory neural networks based on memristor (MMAMNNs). That is, the method of impulsive and feedback control combing with the (event) self-triggered mechanism is adopted. However some projective synchronization errors based on state related parameters of MMAMNNs will be affected by the diverse initial conditions. Thus, the new criterion is supported by establishing a novel Lyapunov function combined with the features of such diverse parameters and systems. A collaborative proposed method is designed to make the error of such system converging to zero. Then, the Zeno-behavior is testified to disappear from the proposed programs. Finally, some examples demonstrate the validity of the proposed method and to show its potential application in image protection.

Suggested Citation

  • Wang, Weiping & Sun, Yue & Yuan, Manman & Wang, Zhen & Cheng, Jun & Fan, Denggui & Kurths, Jürgen & Luo, Xiong & Wang, Chunyang, 2021. "Projective synchronization of memristive multidirectional associative memory neural networks via self-triggered impulsive control and its application to image protection," Chaos, Solitons & Fractals, Elsevier, vol. 150(C).
    • Handle: RePEc:eee:chsofr:v:150:y:2021:i:c:s0960077921004641
    DOI: 10.1016/j.chaos.2021.111110
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    References listed on IDEAS

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    1. Yuan, Manman & Wang, Weiping & Luo, Xiong & Liu, Linlin & Zhao, Wenbing, 2018. "Finite-time anti-synchronization of memristive stochastic BAM neural networks with probabilistic time-varying delays," Chaos, Solitons & Fractals, Elsevier, vol. 113(C), pages 244-260.
    2. Wenjiao Sun & Guojian Ren & Yongguang Yu & Xudong Hai, 2020. "Global Synchronization of Reaction-Diffusion Fractional-Order Memristive Neural Networks with Time Delay and Unknown Parameters," Complexity, Hindawi, vol. 2020, pages 1-14, May.
    3. Mathiyalagan, K. & Park, Ju H. & Sakthivel, R., 2015. "Synchronization for delayed memristive BAM neural networks using impulsive control with random nonlinearities," Applied Mathematics and Computation, Elsevier, vol. 259(C), pages 967-979.
    4. E. L. Pankratov & B. Spagnolo, 2005. "Optimization of impurity profile for p-n-junction in heterostructures," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 46(1), pages 15-19, July.
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    Cited by:

    1. Ganesan, Bhuvaneshwari & Annamalai, Manivannan, 2023. "Anti-synchronization analysis of chaotic neural networks using delay product type looped-Lyapunov functional," Chaos, Solitons & Fractals, Elsevier, vol. 174(C).
    2. Kumar, Ankit & Das, Subir & Singh, Sunny & Rajeev,, 2023. "Quasi-projective synchronization of inertial complex-valued recurrent neural networks with mixed time-varying delay and mismatched parameters," Chaos, Solitons & Fractals, Elsevier, vol. 166(C).
    3. Karnan, A. & Nagamani, G., 2023. "Event-triggered extended dissipative synchronization for delayed neural networks with random uncertainties," Chaos, Solitons & Fractals, Elsevier, vol. 175(P1).
    4. Yuan, Manman & Luo, Xiong & Mao, Xue & Han, Zhen & Sun, Lei & Zhu, Peican, 2022. "Event-triggered hybrid impulsive control on lag synchronization of delayed memristor-based bidirectional associative memory neural networks for image hiding," Chaos, Solitons & Fractals, Elsevier, vol. 161(C).

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