TY - GEN
T1 - Practical stability analysis for DNN observation
AU - Chairez, I.
AU - Poznyak, A.
AU - Poznyak, T.
PY - 2008
Y1 - 2008
N2 - The most important fact for differential neural networks dynamics is related to its weights time evolution. This is a consequence for the higher nonlinear structure describing the matrix differential equations, which are associated with the adaptive capability for this kind of neural networks. However, as we know, there is no any analytical demonstration of the weights stability. In fact, this is the main inconvenient to design real applications of differential neural network observers, especially for control uncertain nonlinear systems. This paper deals with the stability proof for the weights dynamics using an adaptive procedure to adjust the weights ODE. A new dynamic neuro-observer, using the classical Luenberger structure based on practical stability theory, is suggested This methodology aviods the averaged convergence for the state estimation and provides an upper bound for the weights trajectories. A numerical example dealing with the ozonization process state estimation is presented to illustrate the effectiveness of the suggested approach.
AB - The most important fact for differential neural networks dynamics is related to its weights time evolution. This is a consequence for the higher nonlinear structure describing the matrix differential equations, which are associated with the adaptive capability for this kind of neural networks. However, as we know, there is no any analytical demonstration of the weights stability. In fact, this is the main inconvenient to design real applications of differential neural network observers, especially for control uncertain nonlinear systems. This paper deals with the stability proof for the weights dynamics using an adaptive procedure to adjust the weights ODE. A new dynamic neuro-observer, using the classical Luenberger structure based on practical stability theory, is suggested This methodology aviods the averaged convergence for the state estimation and provides an upper bound for the weights trajectories. A numerical example dealing with the ozonization process state estimation is presented to illustrate the effectiveness of the suggested approach.
UR - http://www.scopus.com/inward/record.url?scp=62949131712&partnerID=8YFLogxK
U2 - 10.1109/CDC.2008.4738995
DO - 10.1109/CDC.2008.4738995
M3 - Contribución a la conferencia
AN - SCOPUS:62949131712
SN - 9781424431243
T3 - Proceedings of the IEEE Conference on Decision and Control
SP - 2551
EP - 2556
BT - Proceedings of the 47th IEEE Conference on Decision and Control, CDC 2008
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 47th IEEE Conference on Decision and Control, CDC 2008
Y2 - 9 December 2008 through 11 December 2008
ER -