Información del artículo
Resumen
La identificación de sistemas complejos y no-lineales ocupa un lugar importante en las arquitecturas de neurocontrol, como por ejemplo el control inverso, control adaptativo directo e indirecto, etc. Es habitual en esos enfoques utilizar redes neuronales “feedforward” con memoria en la entrada (Tapped Delay) o bien redes recurrentes (modelos de Elman o Jordan) entrenadas off-line para capturar la dinámica del sistema (directa o inversa) y utilizarla en el lazo de control. En este artículo presentamos un esquema de identificación basado en redes del tipo RBF (Radial Basis Function) que se entrena on-line y que dinámicamente modifica su estructura (número de nodos o elementos en la capa oculta) permitiendo una implementación en tiempo real del identificador en el lazo de control.
Palabras clave:
Identificación
sistemas no-lineales
redes neuronales
estimación de parámetros
El Texto completo está disponible en PDF
Referencias
[Chen et al., 1990]
S. Chen, S.A. Billings, P.M. Grant.
Nonlinear system identification using neural networks.
International Journal of Control, vol. 51 (1990), pp. 1191-1214
[Chen and Billings, 1994]
Chen, S. y S.A. Billings (1994). Neural Networks for Nonlinear Dynamic System Modelling and Identification. En: Advances in Intelligent Control (C.J. Harris (ed.)) 85–112. Taylor & Francis, London.
[Chi et al., 1990]
S.R. Chi, R. Shoureshi, M. Tenorio.
Neural networks for system identification.
IEEE Control Systems Magazine, 10 (1990), pp. 31-34
[Goodwin and Sin, 1984]
G.C. Goodwin, K.S. Sin.
Adaptive filtering prediction and control.
Englewood Cliffs, NJ, (1984),
[Kuschewski et al., 1993]
G.J. Kuschewski, S. Hui, S.H. Zak.
Application of feedforward neural networks to dynamical system identification and control.
IEEE Trans. Control Systems Technology, 1 (1993), pp. 37-49
[Le Cun, 1985]
Y. Le Cun.
Une procedure d’aprentissage pour reseau a sequil assymetrique.
Proceedings of Cognitiva, 85 (1985), pp. 599-604
[Li et al., 2000]
Y. Li, N. Sundararajan, P. Saratchandran.
Analysis of Minimal Radial Basis Function Network Algorithm for Real-Time Identification of Nonlinear Dynamic Systems.
IEE Proc. on Control Theory and Applications, 147 (2000), pp. 476-484
[Narendra and Parthasarathy, 1989]
Narendra, K.S. y K. Parthasarathy (1989). Backpropagation in dynamical systems containing neural networks. Technical Report 8905, Centre for Systems Science, Department of Electrical Engineering, Yale University, New Haven, CT.
[Narendra and Parthasaraty, 1990]
K.S. Narendra, K. Parthasaraty.
Identification and Control of Dynamical Systems using Neural Networks.
IEEE Transactions on Neural Networks, 1 (1990), pp. 4-27
[Obradovic, 1996]
D. Obradovic.
On-Line Training of Recurrent Neural Networks with Continuous Topology Adaptation.
IEEE Trans. on Neural Networks, 7 (1996), pp. 222-228
[Panchapakesan and Palaniswami, 2002]
C. Panchapakesan, M. Palaniswami.
Effects of Moving the Centres in an RBF Network.
IEEE Transactions on Neural Networks, 13 (2002), pp. 1299-1307
[Parker and Learning logic, 1985]
D.B. Parker, Learning logic.
Technical Report TR-47, Massachusetts Institute of Technology,
[Peng and et al, 2004]
H. Peng, et al.
RBF-ARX model-based nonlinear system modeling and predictive control with application to a NOx decomposition process.
Control Engineering Practice, 12 (2004), pp. 191-203
[Poggio and Girosi., 1989]
Poggio T. y F. Girosi. (1989). A Theory of Networks for Approximation and Learning. En: A.I. Memo No. 1140. Artificial Intelligence Laboratory, M.I.T.
[Rumelhart et al., 1986]
Rumelhart, D.E., G.E. Hinton, y R.J. Williams (1986). Learning internal representations by error propagation. En: ., Parallel Distributed Processing: Explorations in the microstructure of cognition. D.E. Rumelhart and J.L. McClelland, eds Vol 1, Foundations. Cambridge, MA: Bradford Books/ MIT Press
[Sanner and Slotine, 1991]
R.M. Sanner, J.E. Slotine.
Stable Adaptive Control and Recursive Identification Using Radial Gaussian Networks.
Proceedings 30th Conference on Decision and Control’,
[Sjöberg et al., 1995]
J. Sjöberg, Q. Zhang, L. Ljung, et al.
Nonlinear Black-box Modelling in System Identification: a Unified Overview.
Automatica, 31 (1995), pp. 1691-1724
[Werbos, 1974]
P.J. Werbos.
Beyond regression: New tools for prediction and analysis in the behavioral sciences..
Cambridge, MA, (1974),
[Widrow and Stearns, 1985]
B. Widrow, S.D. Stearns.
Adaptive Signal Processing.
Englewood Cliffs.NJ, (1985),
[Zemouri et al., 2003]
R. Zemouri, D. Racoceanu, N. Zerhouni.
Recurrent radial basis function network for timeseries prediction.
Engineering Applications of Artificial Intelligence, 16 (2003), pp. 453-463
Copyright © 2007. Elsevier España, S.L.. Todos los derechos reservados