DocumentCode
2657604
Title
Recurrent neural networks for dynamic system modeling
Author
Si, Jennie ; Pang, Liguang
Author_Institution
Dept. of Electr. Eng., Arizona State Univ., Tempe, AZ, USA
fYear
1993
fDate
25-27 Aug 1993
Firstpage
364
Lastpage
369
Abstract
Stability properties of recurrent neural networks are investigated using Lyapunov stability theory. Two sufficient conditions for the global asymptotic stability of equilibrium points of a class of recurrent neural networks are provided. The applicability of recurrent neural networks for nonlinear dynamic system modeling and control is discussed
Keywords
Lyapunov methods; asymptotic stability; nonlinear dynamical systems; recurrent neural nets; Lyapunov stability; dynamic system modeling; equilibrium points; global asymptotic stability; nonlinear dynamic system; recurrent neural networks; sufficient conditions; Artificial neural networks; Control system synthesis; Control theory; Modeling; Multilayer perceptrons; Nonlinear control systems; Nonlinear dynamical systems; Nonlinear systems; Recurrent neural networks; Stability;
fLanguage
English
Publisher
ieee
Conference_Titel
Intelligent Control, 1993., Proceedings of the 1993 IEEE International Symposium on
Conference_Location
Chicago, IL
ISSN
2158-9860
Print_ISBN
0-7803-1206-6
Type
conf
DOI
10.1109/ISIC.1993.397686
Filename
397686
Link To Document