Global exponential stability of reaction–diffusion neural networks with discrete and distributed time-varying delays

This paper investigates the global exponential stability of reaction–diffusion neural networks with discrete and distributed time-varying delays. By constructing a more general type of Lyapunov–Krasovskii functional combined with a free-weighting matrix approach and analysis techniques, delay-dependent exponential stability criteria are derived in the form of linear matrix inequalities. The obtained results are dependent on the size of the time-varying delays and the measure of the space, which are usually less conservative than delay-independent and space-independent ones. These results are easy to check, and improve upon the existing stability results. Some remarks are given to show the advantages of the obtained results over the previous results. A numerical example has been presented to show the usefulness of the derived linear matrix inequality (LMI)-based stability conditions.