Asymptotic stability analysis of neural networks with successive time delay components

In this paper the asymptotic stability of a class of time-delay neural networks is investigated. The neural network model under consideration includes multiple components which is more general than those with the single delay. By constructing a new Lyapunov functional and by using advanced techniques for achieving delay dependence, we derive a new asymptotic stability criterion for neural networks with multiple successive delay components. A numerical example is provided to show the merits of the proposed criterion.