Vande Wouwer, Alain ; Université de Mons > Faculté Polytechnique > Systèmes, Estimation, Commande et Optimisation
Renotte, Christine ; Université de Mons > Faculté Polytechnique > Systèmes, Estimation, Commande et Optimisation ; Université de Mons > Faculté Polytechnique > Service de Qualité, Accompagnement, Pédagogie
Remy, Marcel ; Université de Mons > Faculté Polytechnique > Systèmes, Estimation, Commande et Optimisation
Language :
English
Title :
Application of stochastic approximation techniques in neural modeling and control
Publication date :
01 January 2003
Journal title :
International Journal of Systems Science
ISSN :
0020-7721
Publisher :
Taylor & Francis, United Kingdom
Issue :
34
Pages :
851-863
Peer reviewed :
Peer Reviewed verified by ORBi
Research unit :
F107 - Systèmes, Estimation, Commande et Optimisation
Ayoubi, M., 1996, Nonlinear System Identification Based on Neural Networks with Locally Distributed Dynamics and Application to Technical Processes (Dusseldorf: VDI).
Hunt, K. J., Sbardaro, D., Zbikowski, R., and Gawthrop, P. J., 1992, Neural network for control systems-a survey. Automatica, 28, 1083-1112.
Jenson, V. G., and Jeffreys, G. V., 1977, Mathematical Methods in Chemical Engineering (New York: Academic Press).
Ljung, L., 1987, System Identification, Theory for the User (Eaglewood Cliffs: Prentice Hall).
Narendra, K. S., and Parthasarathy, K., 1990, Identification and control of dynamical systems using neural networks. IEEE Transactions on Neural Networks, 1, 4-27.
Narendra, K. S., and Parthasarathy, K., 1991, Gradient methods for the optimization of dynamical systems containing neural networks. IEEE Transactions on Neural Networks, 2, 252-262.
Patwardhan, A. A., Rawlings, J. B., and Edgar, T. F., 1990, Nonlinear model predictive control. Chemical Engineering Communications, 87, 123-141.
Reklaitis, G. V., Ravindran, A., and Ragsdell, K. M., 1983, Engineering Optimization-Methods and Applications (Chichester: Wiley).
Rumelhart, D. E., Hinton, G. E., and Williams, R. J., 1986, Learning representations by back-propagating errors. Nature, 323, 533-536.
Spall, J. C., 1992, Multivariate stochastic approximation using a simultaneous perturbation gradient approximation. IEEE Transactions on Automatic Control, 37, 332-341.
Spall, J. C., 2000, Adaptive stochastic approximation by the simultaneous perturbation method. IEEE Transactions on Automatic Control, 45, 1839-1853.
Spall, J. C., and Cristion, J. A., 1994, Nonlinear adaptive control using neural networks: estimation with smoothed form of simultaneous perturbation gradient approximation. Statistica Sinica, 4, 1-27.
Suykens, J., Vandewalle, J., and De Moor, B., 1996, Artificial Neural Networks for Modelling and Control of Non-Linear Systems (Dordrecht: Kluwer).
Tan, Y., 1993, An architecture for adaptive neural control. Journal A, 34, 12-16.
Werbos, P. J., 1990, Backpropagation through time: what it does and how to do it. Proceedings of the IEEE, 78, 1550-1560.
Zhu, X., and Spall, J. C., 2002, A modified second order SPSA optimization algorithm for finite samples. Int. J. of Adaptive Control and Signal Processing, 16, 397-409.