A Novel Time-Delay Recurrent Neural Network and Application for Identifying and Controlling Nonlinear Systems

A time-delay recurrent neural network (TDRNN) model is proposed. TDRNN has a simple structure but far more "depth" and "resolution ratio" in memory by introducing the time-delay and recurrent mechanism. A dynamic recurrent back propagation algorithm is developed. The optimal adaptive learning rates are derived in the sense of discrete-type Lyapunov stability to guarantee the fast convergence of the proposed model. More specifically, a TDRNN identifier and a TDRNN controller are utilized for identifying and controlling nonlinear systems. Numerical experiments show that the TDRNN has good effectiveness in the identification and control for dynamic systems.