Synchronization of Coupled Reaction-Diffusion Neural Networks Driven by G-Brownian Motion with Time-Varying Delay

The objective of this paper is to explore the issue of exponential synchronization in coupled reaction-diffusion stochastic neural networks driven by G-Brownian motion with time-varying delay (TVD). By employing the Lyapunov function approach and G-Itô's formula and Poincare-type inequality, we establish appropriate synchronization criteria based on the developed impulsive pinning control strategies, which serve as a sufficient condition. Our findings demonstrate the feasibility of achieving exponential synchronization in coupled reaction-diffusion stochastic neural networks with G-Brownian motion by controlling a limited number of network nodes with delayed impulses. Additionally, we provide an illustrative numerical example to demonstrate the effectiveness and feasibility of the proposed method.