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48. Press W.H., Teukolsky S.A., Vetterling W.T., Flannery B.P. [1992],
Numerical
Recipes in C: The Art of Scientific Computing
, Cambridge University Press.
49. Puskorius G.V., Feldkamp L.A. [1994], Neurocontrol of nonlinear dynamical
systems with Kalman Filter trained recurrent networks,
IEEE Trans. on Neural
Networks
, 5, pp 279-297
50. Rumelhart D.E., Hinton G.E., Williams R.J. [1986], Learning internal represen-
tations by error backpropagation,
Parallel Distributed Processing: Explorations
in the Microstructure of Cognition
, pp 318-362, MIT Press
51. Saarinen S., Bramley R., Cybenko G. [1993], Ill-conditioning in neural network
training problems,
SIAM J. Sci. Stat. Comp.
, 14, pp 693-714
52. Seber G.A.F., Wilde C.J. [1989],
Nonlinear Regression
, Wiley
53. Seber G.A.F. [1977],
Linear Regression Analysis
, Wiley
54. Sjo berg J., Zhang Q., Ljung L., Benveniste A., Delyon B., [1995], Nonlinear
black-box modeling in system identification: a unified overview,
Automatica
, 31,
pp 1691-1724
55. Soderstrom T. [1977], On model structure testing in system identification,
In-
ternational Journal of Control
, 26, pp 1-18
56. Sontag E.D. [1993], Neural networks for control,
Essays on control: perspectives
in the theory and its applications
, pp 339-380, Birkhauser
57. Stone M. [1974], Cross-validatory choice and assessment of statistical predic-
tions,
Journal of the Royal Statistical Society
, B 36, pp 111-147
58. Stoppiglia H. [1998],
Methodes statistiques de selection de modeles neuronaux;
applications financieres et bancaires
,these de doctorat de l'Universite Pierre et
Marie-Curie.
Available from
http://www.neurones.espci.fr
59. Stoppiglia H., Dreyfus G., Dubois R., Oussar Y. [2003], Ranking a Random Fea-
ture for Variable and Feature Selection,
Journal of Machine Learning Research
,
pp 1399-1414
60. Stricker M. [2000],
Reseaux de neurones pour le traitement automatique du lan-
gage: conception et realisation de filtres d'informations
,these de l'Universite
Pierre et Marie-Curie.
Available from
http://www.neurones.espci.fr
61. Tibshirani R.J. [1996], A comparison of some error estimates for neural models,
Neural Computation
, 8, pp 152-163
62. Tikhonov A.N., Arsenin V.Y. [1977],
Solutions of Ill-Posed Problems
,Winston
63. Vapnik V.N. [1995],
The Nature of Statistical Learning Theory
, Springer
64. Waibel , Hanazawa T., Hinton G., Shikano K., and Lang K. [1989], Phoneme
recognition using time-delay neural networks,
IEEE Transactions on Acoustics,
Speech, and Signal Processing
, 37, pp 328-339
65. Werbos P.J. [1974],
Beyond regression: new tools for prediction and analysis in
the behavioural sciences
, Ph. D. thesis, Harvard University
66. Widrow B., Hoff M.E. [1960], Adaptive switching circuits,
IRE Wescon Con-
vention Records,
4, pp 96-104
67. Wonnacott T.H., Wonnacott R.J. [1990],
Statistique economie-gestion-sciences-
medecine
, Economica, 4
e
edition, 1990
68. Zhou G., Si J. [1998], A systematic and effective supervised learning mechanism
based on Jacobian rank deficiency,
Neural Computation
, 10, pp 1031-1045
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