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30. Ljung L., Sjoberg J., Hjalmarsson H. [1996], On neural network model structures
in system identification, in Identification, Adaptation, Learning. The science of
learning models from data , S. Bittanti, G. Pici, ed., NATO ASI Series, Springer
31. Nerrand O., Roussel-Ragot P., Personnaz L., Dreyfus G. [1993], Neural networks
and nonlinear adaptive filtering: unifying concepts and new algorithms, Neural
Computation , 5, pp 165-199
32. Nerrand O., Roussel-Ragot P., Urbani D., Personnaz L., Dreyfus G. [1994],
Training recurrent neural networks: why and how ? An illustration in dynamical
processes modeling, IEEE Transactions on neural networks , 5.2, pp 178-184
33. Norgaard M., Ravn O., Poulsen N.K., Hansen L.K. [2000], Neural Networks for
Modelling and Control of Dynamical Systems , Springer
34. Puskorius G.V., Feldkamp L.A. [1994], Neurocontrol of nonlinear dynamical
systems with Kalman filter-trained recurrent networks, IEEE Transactions on
Neural Networks , vol. 5, pp 279-297
35. Rivals I. [1995], Modelisation et commande de processus par reseaux de neu-
rones; application au pilotage d'un vehicule autonome, these de doctorat de
l'Universite Pierre et Marie-Curie, Paris VI
36. Rivals I., Personnaz L. [2000], Nonlinear Internal Model Control Using Neural
Networks, IEEE Transactions on Neural Networks , vol. 11, pp 80-90
37. Singh S.P., Jaakkola T., Jordan M. [1995], Learning without state estimation
in a partially observable Markov decision problems, Proceedings of the 11th
Machine Learning conference
38. Slotine J.J.E., Li W. [1991], Applied Nonlinear Control , Prentice Hall
39. Slotine J.J.E., Sanner R.M. [1993], Neural Networks for Adaptive Control and
Recursive Identification: A Theoretical Framework, in Essays on Control ,H.L.
Trentelman, J.C. Willems, ed., Birkhauser, pp 381-435
40. Sontag E.D. [1990], Mathematic Control Theory. Deterministic finite dimen-
sional systems , Springer Verlag
41. Sontag E.D. [1996], Recurrent Neural Networks: Some Systems-Theoretic As-
pects , Dept. of Mathematics, Rutgers University, NB, Etats-Unis
42. Sutton R.S. [1988], Learning to predict by the method of temporal differences,
Machine Learning , 3, pp 9-44
43. Thrun S.B. [1992], The role of exploration in learning control, in Handbook of
intelligent control , D.A. White, D.A. Sofge, ed., pp 527-559, Van Nostrand
44. Tong H. [1995], Nonlinear Time Series, a dynamical system approach , Clarendon
Press
45. Urbani D., Roussel-Ragot P., Personnaz L., Dreyfus G. [1993], The selection
of nonlinear dynamical systems by statistical tests, Neural Networks for Signal
Processing , 4, pp 229-237
46. Watkins C.J.C.H., Dayan P. [1992] Q-learning, Machine Learning , 8, pp 279-292
47. Williams, R.J., Zipser, D. [1989], “A learning algorithm for continully runnig
fully recurrent neural networks”, Neural Computation , pp. 270-280
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