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

Authors

Wang,  Weiping
External Organizations;

Sun,  Yue
External Organizations;

Yuan,  Manman
External Organizations;

Wang,  Zhen
External Organizations;

Cheng,  Jun
External Organizations;

Fan,  Denggui
External Organizations;

/persons/resource/Juergen.Kurths

Kurths,  Jürgen
Potsdam Institute for Climate Impact Research;

Luo,  Xiong
External Organizations;

Wang,  Chunyang
External Organizations;

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Citation

Wang, W., Sun, Y., Yuan, M., Wang, Z., Cheng, J., Fan, D., Kurths, J., Luo, X., Wang, C. (2021): Projective synchronization of memristive multidirectional associative memory neural networks via self-triggered impulsive control and its application to image protection. - Chaos, Solitons and Fractals, 150, 111110.
https://doi.org/10.1016/j.chaos.2021.111110


Cite as: https://publications.pik-potsdam.de/pubman/item/item_26320
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.