Adaptive synchronization of Cohen–Grossberg neural networks with unknown parameters and mixed time-varying delays

In this paper, we investigate the synchronization problem of chaotic Cohen–Grossberg neural networks with unknown parameters and mixed time-varying delays. An adaptive linear feedback controller is designed to guarantee that the response system can be synchronized with a drive system by utilizing Lyapunov stability theory and parameter identification. Our synchronization criteria are easily verified and do not need to solve any linear matrix inequality. These results generalize a few previous known results and remove some restrictions on amplification function and time delay. This research also demonstrates the effectiveness of application in secure communication. Numerical simulations are carried out to illustrate the main results.

► Adaptive controller to guarantee the synchronization of neural networks is obtained. ► The parameter identification method is applied to estimate the unknown parameters. ► This research shows the effectiveness of application in secure communication. ► An example is given to demonstrate the feasibility of the main results.