Generalized Projective Synchronization of Time-Delayed Chaotic Systems via Sliding Adaptive Radial Basis Function Neural Network Control

In this study, generalized projective synchronization (GPS) of two identical and nonidentical time-delayed chaotic systems is presented. Sliding adaptive radial basis function neural network control (SARBFNNC) is applied to synchronize two delayed chaotic systems. The advantages of the adaptive control, neural network and sliding mode control theory are combined in the proposed method. The stability of error dynamics is guaranteed with Lyapunov stability theory. Moreover, supposing that the parameters of the chaotic system are unknown, recursive least square (RLS) method is applied to estimate these unknown parameters. The proposed method has not been used for synchronization of time-delayed chaotic systems yet. Simulation results show that the proposed method is suitable and effective for synchronization of time-delayed chaotic systems.

Keywords:

Generalized projective synchronization , Time-delayed chaotic systems , Sliding mode control , Radial basis function neural network , Adaptive neural network