Boundary intermittent stabilization for delay reaction–diffusion cellular neural networks

Li, Xingyu; Fan, Qing-Ling; Liu, Xiaozhen; Wu, Kai‐Ning

doi:10.1007/s00521-022-07457-1

Cited by 5 publications

(1 citation statement)

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“…Cohen-Grossberg NNs (CGNNs) was initially proposed and studied by Cohen and Grossberg in 1983 [12]. Some other models, cellular NNs [13], Hopfield NNs [14], and recurrent NNs [15] are special cases of CGNNs. As a dynamical behavior in CGNNs, stabilization plays an unique role in various fields such as engineering and science.…”

Section: Introductionmentioning

confidence: 99%

Event-triggered controller on practically exponential input-to-state stabilization of stochastic reaction-diffusion neural networks and its application to image encryption

Gokulakrishnan

Sriniva

2023

Preprint

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The stabilization problem for a class of stochastic reaction-diffusion delayed Cohen-Grossberg neural networks (SRDDCGNNs) with event-triggered controller is addressed in this paper. To address such a problem, Neumann boundary condition, distributed and boundary external disturbances are introduced. New sufficient criteria are derived by using the Lyapunov method, event-triggered mechanism, and the linear matrix inequality (LMI) approach to ensure the proposed controlled systems achieve practically exponential input-to-state stabilization. In light of these criteria, the impact of an event-triggered controller on practically exponential input-to-state stability (PEISS) is examined. Furthermore , the obtained results are successfully applied to stochastic reaction-diffusion delayed cellular neural networks (SRDDCNNs) and stochastic reaction-diffusion delayed Hopfield neural networks (SRD-DHNNs). At last, simulation results are given to illustrate the main results, and the SRDDHNNs are applied to the image encryption.

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