New exponential stability results for delayed neural networks with time varying delays

This paper presents new sufficient conditions for the uniqueness and exponential stability of the equilibrium point for delayed neural networks with time varying delays. By employing a more general type of Lyapunov–Krasovskii functional and using LMI (linear matrix inequality), we derive new results for exponential stability of the equilibrium point for delayed neural networks. The results establish a relation between the delay time and the parameters of the network. The results are compared with the previous results derived in the literature.