New results for robust stability of dynamical neural networks with discrete time delays

This paper deals with the global robust asymptotic stability of the equilibrium point of class of delayed neural networks having uncertain parameters whose values are unknown but bounded. By introducing a new upper bound norm for the interconnection matrix of the neural system and employing suitable Lyapunov functionals, we obtain new delay independent sufficient conditions for the uniqueness and global robust asymptotic stability of the equilibrium point. The obtained results can be easily verified as they can be expressed in terms of the network parameters only. Some examples are constructed to compare the reported results with the related existing literature results.