Indexed by:期刊论文

Date of Publication:2009-08-01

Journal:NEUROCOMPUTING

Included Journals:SCIE、EI、CPCI-S、Scopus

Volume:72

Issue:13-15

Page Number:2857-2864

ISSN No.:0925-2312

Key Words:Time-delay recurrent neural network; Nonlinear system; System identification; System control

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. (C) 2009 Published by Elsevier B.V.