Synchronization of Cohen-Grossberg neural networks with time-varying delays via state-constraint impulsive controller

The aim of this paper is to study the synchronization problem of delayed Cohen-Grossberg neural networks with impulse controller, in which the saturation limit of the impulse is taken into account. Firstly, by constructing a suitable Lyapunov-Krasovskii functional candidate and using techniques of inequality and convex analysis, several criteria are obtained to reach the synchronization goal of delayed Cohen-Grossberg neural networks with state-constraint impulsive controller. Based on these criteria, the region of attraction for the synchronization error system is estimated. Secondly, the special case that only part of the states subject from saturation is also considered, and the corresponding results are obtained. Moreover, when Cohen-Grossberg neural networks are simplified to special cases such as Hopfield neural networks, our results have been improved and expanded. Finally, the simulation example shows the effectiveness of the proposed results.