Adaptive Neural Network Matrix Projection Synchronization of Unknown Complex Dynamical Networks with Different Dimensions

Adaptive generalized function matrix projection synchronization (GFMPS) is investigated for the driven-response unknown complex dynamical networks with different dimension of nodes. RBF neural networks are utilized to represent the unknown nonlinear functions. A novel adaptive synchronization method is designed for the network. The adaptive generalized function matrix projection outer synchronization can be achieved in both driver and response networks, the synchronization condition is also given via Lyapunov stability theory. A numerical example is presented to demonstrate the validity of the theoretical results.