Exponential synchronization of neural networks with time-varying mixed delays and sampled-data

Abstract

This paper investigates the problem of exponential synchronization for neural networks with mixed delays using sampled-data feedback control. Lyapunov–Krasovskii functional combining with the input delay approach as well as the improved free-weighting matrix approach are employed to derive several sufficient criteria ensuring the delayed neural networks to be exponentially synchronous. The conditions obtained are dependent not only on the maximum sampling interval but also on the exponential synchronization rate. A numerical example is given to demonstrate the usefulness and merits of the proposed scheme.