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

Abstract

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.

Event-Triggered Adaptive Fuzzy Control for Strict-Feedback Nonlinear FOSs Subjected to Finite-Time Full-State Constraints

“…The fractional-order systems (FOSs) with infinite memory and genetic characteristics can describe the more nonclassic phenomenon in the physical systems [1,2], which have been applied in a lot of different areas such as secret communication [3,4], vehicle [5,6], circuit [7,8], finance [9,10], image [11][12][13], etc.…”