Global robust stability analysis of neural networks with discrete time delays

Global robust convergence properties of continuous-time neural networks with discrete delays are studied. By using a Lyapunov functional, we derive a delay independent stability condition for the existence uniqueness and global robust asymptotic stability of the equilibrium point. The condition is in terms of the network parameters only and can be easily verified. It is also shown that the obtained result improves and generalizes a previously published result.