Title :
Using neural networks to estimate regions of stability
Author :
Ferreira, Enrique D. ; Krogh, Bruce H.
Author_Institution :
Dept. of Electr. & Comput. Eng., Carnegie Mellon Univ., Pittsburgh, PA, USA
Abstract :
This paper presents a new method to estimate the region of stability of an asymptotically stable equilibrium point of an autonomous nonlinear system using a neural network. In contrast to model-based analytical methods, this approach uses empirical data from the system to train the neural network. The neural network results are compared with estimates obtained by previously proposed methods for some samples of two dimensional problems and for an inverted pendulum
Keywords :
asymptotic stability; feedforward neural nets; intelligent control; learning (artificial intelligence); neurocontrollers; nonlinear systems; pendulums; asymptotic stability; autonomous nonlinear system; equilibrium point; feedforward neural networks; inverted pendulum; learning; neurocontrol; Analytical models; Asymptotic stability; Control system synthesis; Control systems; Lyapunov method; Neural networks; Nonlinear systems; Power system dynamics; Power system stability; Real time systems;
Conference_Titel :
American Control Conference, 1997. Proceedings of the 1997
Conference_Location :
Albuquerque, NM
Print_ISBN :
0-7803-3832-4
DOI :
10.1109/ACC.1997.611036
