Stability and synchronization of delayed fractional‐order reaction‐diffusion Cohen–Grossberg neural networks under perturbation and parameter mismatch

Abstract This study investigates the synchronization and stability of delayed fractional‐order Cohen–Grossberg neural networks (FOCGNNs) with parameter mismatches and external perturbations. By employing fractional‐order comparison principles and Dirichlet boundary conditions, we establish novel sufficient conditions that ensure the global uniformly stable of such networks. The obtained stability results substantially improve and generalize prior results. Furthermore, we propose two distinct control strategies to address the synchronization problem in FOCGNNs. To validate the effectiveness of the theoretical findings, we present two numerical examples along with their corresponding simulations.