Identification and control of nonlinear systems by a time-delay recurrent neural network

Authors:
Hong-Wei Ge;Wen-Li Du;Feng Qian;Yan-Chun Liang
Affiliations:
College of Electronic and Information Engineering, Dalian University Of Technology, Dalian 116024, China and Automation Institute, East China University of Science and Technology, Shanghai 200237, ...;Automation Institute, East China University of Science and Technology, Shanghai 200237, China;Automation Institute, East China University of Science and Technology, Shanghai 200237, China;College of Computer Science and Technology, Jilin University, Changchun 130012, China
Venue:
Neurocomputing
Year:
2009

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Abstract

In this paper, we first present a novel time-delay recurrent neural network (TDRNN) model by introducing the time-delay and recurrent mechanism. The proposed TDRNN model has special advantages such as simple structure, deeper depth and higher resolution ratio in memory. Thereafter, we develop the dynamic recurrent back-propagation algorithm for the TDRNN. To guarantee the fast convergence, the optimal adaptive learning rates are also derived in the sense of discrete-type Lyapunov stability. More specifically, a TDRNN identifier and a TDRNN controller are constructed to perform the identification and control of the nonlinear systems. Numerical experiments show that the TDRNN model has good effectiveness in the identification and control for dynamic systems.