Stabilization effect of diffusion in delayed neural networks systems with Dirichlet boundary conditions

In this paper, reaction–diffusion neural networks with unbounded time-varying delays and Dirichlet boundary conditions is studied. A new concept of global μ - stability in the sense of L2 norm is introduced, and sufficient conditions are given to guarantee global μ - stability of the equilibrium point. The results obtained not only improve those in the earlier findings, but also show diffusion terms contribute to stabilization of neural networks systems.