DocumentCode
2900828
Title
Stabilization of stochastic recurrent neural networks
Author
Sanchez, Edgar N. ; Perez, Jose P.
Author_Institution
CINVESTAV, Unidad Guadalajara, Mexico
fYear
2002
fDate
2002
Firstpage
445
Lastpage
447
Abstract
The paper presents the stabilization of a dynamic neural network disturbed by additive Gaussian noise. This stabilization is achieved using a quadratic Lyapunov function. A control law is derived, which ensures that the neural network state becomes globally uniform ultimately bounded (GUUB) in probability. The applicability of the proposed control law is illustrated by an example.
Keywords
Gaussian noise; Lyapunov methods; control system synthesis; neurocontrollers; nonlinear control systems; recurrent neural nets; stability; stochastic systems; additive Gaussian noise; control design; control law; dynamic neural network; globally uniform ultimately bounded neural network state; nonlinear systems; quadratic Lyapunov function; stochastic control Lyapunov function; stochastic recurrent neural network stabilization; Additive noise; Associative memory; Electronic mail; Lyapunov method; Neural networks; Nonlinear systems; Recurrent neural networks; Stability analysis; Stochastic processes; Stochastic resonance;
fLanguage
English
Publisher
ieee
Conference_Titel
Intelligent Control, 2002. Proceedings of the 2002 IEEE International Symposium on
ISSN
2158-9860
Print_ISBN
0-7803-7620-X
Type
conf
DOI
10.1109/ISIC.2002.1157804
Filename
1157804
Link To Document