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
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;
Conference_Titel :
Intelligent Control, 1993., Proceedings of the 1993 IEEE International Symposium on
Conference_Location :
Chicago, IL
Print_ISBN :
0-7803-1206-6
DOI :
10.1109/ISIC.1993.397686
