Robust stability analysis of a class of delayed neural networks

Abstract

This paper studies the global robust stability of delayed neural networks. A new sufficient condition that ensures the existence, uniqueness and global robust asymptotic stability of the equilibrium point is presented. The obtained condition is derived by using the Lyapunov stability and Homomorphic mapping theorems and by employing the Lipschitz activation functions. The result presented establishes a relationship between the network parameters of the neural system independently of time delays. We show that our results is new and improves some of the previous global robust stability results expressed for delayed neural networks.