Event-Triggered Controller on Practically Exponential Input-to-State Stabilization of Stochastic Reaction–Diffusion Cohen–Grossberg Neural Networks and Its Application to Image Encryption

The stabilization problem for a class of stochastic reaction–diffusion delayed Cohen–Grossberg neural networks (SRDDCGNNs) with event-triggered controllers is addressed in this paper. Neumann boundary conditions, distributed and bounded external disturbances are introduced to solve such a problem. New sufficient criteria are derived using the 2-norm event generator and Lyapunov functional to ensure that the proposed controlled systems achieve practically exponential input-to-state stabilization in terms of the linear matrix inequality. Considering these criteria, the impact of an event-triggered controller on the practically exponential input-to-state stability is investigated. The Zeno phenomenon of the event-triggered controller is avoided. Moreover, the obtained results are successfully applied to SRDDCNNs and stochastic reaction–diffusion delayed Hopfield neural networks (SRDDHNNs). Finally, the main results are illustrated with simulation results and the SRDDHNNs are applied to image encryption.