Title :
Discussion of stability on recurrent neural networks for nonlinear dynamic systems
Author :
Lisang, Liu ; Xiafu, Peng
Author_Institution :
Dept. of Electron. Inf. & Electr., Fujian Univ. of Technol., Fuzhou, China
Abstract :
Stability analysis is a most important problem in the dynamic analysis of dynamical systems. The stability properties and dynamic behavior of the recurrent neural network for nonlinear dynamic system modeling directly determine its engineering applications. In this paper, based on Lyapunov stability theory, the stability problems of recurrent neural networks (RNN) and its general stability conditions are discussed. And a novel diagonal recurrent neural network with output feedback (O-DRNN) is proposed as an concrete example, analyzing its stability as well as the range of learning rate.
Keywords :
Lyapunov methods; feedback; nonlinear dynamical systems; recurrent neural nets; Lyapunov stability theory; O-DRNN; RNN; diagonal recurrent neural network-with-output feedback; dynamical system dynamic analysis; engineering applications; learning rate; nonlinear dynamic system modeling; stability analysis; stability conditions; Learning systems; Lyapunov methods; Mathematical model; Nonlinear dynamical systems; Recurrent neural networks; Stability criteria; diagonal recurrent neural network; nonlinear dynamic system; stability;
Conference_Titel :
Computer Science & Education (ICCSE), 2012 7th International Conference on
Conference_Location :
Melbourne, VIC
Print_ISBN :
978-1-4673-0241-8
DOI :
10.1109/ICCSE.2012.6295045
