Pinning sampled-data synchronization of coupled inertial neural networks with reaction-diffusion terms and time-varying delays

The intention of this paper is to explore the problem of pinning sampled-data synchronization of coupled reaction-diffusion neural networks with added inertia and time-varying delays. Through the proper variable substitution, the original system is transferred into first-order differential equations. Then, by constructing a suitable Lyapunov-Krasovskii functional (LKF), which uses more information of the delay bounds, global asymptotic synchronization criteria for the considered system are established in the form of LMIs. The acquired LMIs can be simply examined for their practicability by utilizing any of the accessible softwares. At last, two examples are furnished to manifest the efficacy of the derived criteria.