NEW SUFFICIENT CRITERIA FOR GLOBAL ROBUST STABILITY OF NEURAL NETWORKS WITH MULTIPLE TIME DELAYS

NEW SUFFICIENT CRITERIA FOR GLOBAL ROBUST STABILITY OF NEURAL NETWORKS WITH MULTIPLE TIME DELAYS

Eylem Yucel    Sabri Arik    

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Abstract

In this paper, we study global robust asymptotic stability of the equilibrium point for neural networks with multiple time delays. By employing suitable Lyapunov functionals, we derive a set of delay independent sufficient conditions for global robust asymptotic stability of this class of neural networks. Some examples are constructed to compare the reported results with the related existing results. This comparison proves that our results establish a new set of robust stability criteria for delayed neural networks. It is also demonstrated that the reported results can be easily verified as they can be expressed in terms of the network parameters only.

Keywords

Neural networks, robust stability, delayed systems, Lyapunov functionals, interval matrices.