TY - GEN
T1 - Discrete time recurrent neural network observer
AU - Salgado, I.
AU - Chairez, I.
PY - 2009
Y1 - 2009
N2 - State estimation for uncertain systems affected by external noises is an important problem in control theory. This paper deals with the state observation problem when the dynamic model of a plant contains uncertainties or is completely unknown and it is oriented to discrete time nonlinear systems because most of the existent results have been developed for continuous time systems. The recurrent neural network (RNN) have shown his advantages to deal with this class of problem. The Lyapunov second method is applied to generate a new learning law, containing an adaptive adjustment rate, implying the stability condition for the free parameters of the neuralobserver. A numerical example is given using the RNN in the estimation of a mathematical model of HIV infection with three states.
AB - State estimation for uncertain systems affected by external noises is an important problem in control theory. This paper deals with the state observation problem when the dynamic model of a plant contains uncertainties or is completely unknown and it is oriented to discrete time nonlinear systems because most of the existent results have been developed for continuous time systems. The recurrent neural network (RNN) have shown his advantages to deal with this class of problem. The Lyapunov second method is applied to generate a new learning law, containing an adaptive adjustment rate, implying the stability condition for the free parameters of the neuralobserver. A numerical example is given using the RNN in the estimation of a mathematical model of HIV infection with three states.
KW - Discrete-time recurrent neural network
KW - State estimation and HIV infection
UR - http://www.scopus.com/inward/record.url?scp=70449553845&partnerID=8YFLogxK
U2 - 10.1109/IJCNN.2009.5178900
DO - 10.1109/IJCNN.2009.5178900
M3 - Contribución a la conferencia
AN - SCOPUS:70449553845
SN - 9781424435531
T3 - Proceedings of the International Joint Conference on Neural Networks
SP - 2764
EP - 2770
BT - 2009 International Joint Conference on Neural Networks, IJCNN 2009
T2 - 2009 International Joint Conference on Neural Networks, IJCNN 2009
Y2 - 14 June 2009 through 19 June 2009
ER -
