Generalized synchronization of different dimensional neural networks and its applications in secure communication

This paper analyzes the generalized synchronization of chaotic neural networks and proposes an approach based on the Lyapunov stability theory. This approach realizes the generalized synchronization between two different dimensional neural networks. Numerical simulation examples are given to demonstrate the effectivenes of this approach. By the chaos-masking method, the simulation experiment about generalized synchronization of different dimensional neural networks in secure communication has verified the validity of the approach.