D. H. Ackley, G. E. Hinton, and T. J. Sejnowski, A Learning Algorithm for Boltzmann Machines*, Cognitive Science, vol.85, issue.1, pp.147-169, 1985.
DOI : 10.1207/s15516709cog0901_7

Y. Bengio, Gradient-Based Optimization of Hyperparameters, Neural Computation, vol.58, issue.8, pp.1889-1900, 2000.
DOI : 10.1038/317314a0

S. Chatterjee and S. Bandopadhyay, Reliability estimation using a genetic algorithm-based artificial neural network: An application to a load-haul-dump machine, Expert Systems with Applications, vol.39, issue.12, pp.10943-10951, 2012.
DOI : 10.1016/j.eswa.2012.03.030

R. Dorbritz, Methodology for assessing the structural and operational robustness of railway networks, 2012.

A. A. Ferreira, T. B. Ludermir, and R. R. De-aquino, An approach to reservoir computing design and training, Expert Systems with Applications, vol.40, issue.10, 2013.
DOI : 10.1016/j.eswa.2013.01.029

O. Fink and U. Weidmann, Predicting potential railway operations disruptions caused by critical component failure using echo state neural networks and automatically collected diagnostic data, 92nd Annual Meeting of the Transportation Research Board, p.443, 2013.

P. Green and B. Silverman, Nonparametric regression and generalized linear models: A roughness penalty approach, 1994.
DOI : 10.1007/978-1-4899-4473-3

S. S. Haykin, Neural networks and learning machines, 2009.

A. E. Hoerl and R. W. Kennard, Ridge Regression: Biased Estimation for Nonorthogonal Problems, Technometrics, vol.24, issue.1, pp.55-67, 1970.
DOI : 10.2307/1909769

K. Hornik, M. Stinchcombe, and H. White, Multilayer feedforward networks are universal approximators, Multilayer feedforward networks are universal approximators, pp.359-366, 1989.
DOI : 10.1016/0893-6080(89)90020-8

G. Huang, Learning capability and storage capacity of two-hidden-layer feedforward networks, IEEE Transactions on Neural Networks, vol.14, issue.2, pp.274-281, 2003.
DOI : 10.1109/TNN.2003.809401

H. Jaeger, Tutorial on training recurrent neural networks, covering bptt, rtrl, ekf and the " echo state network " approach, 2005.

H. Jaeger and H. Haas, Harnessing Nonlinearity: Predicting Chaotic Systems and Saving Energy in Wireless Communication, Science, vol.304, issue.5667, pp.78-80, 2004.
DOI : 10.1126/science.1091277

H. Jaeger, M. Lukosevicius, D. Popovici, and U. Siewert, Optimization and applications of echo state networks with leaky- integrator neurons, Neural Networks, vol.20, issue.3, pp.335-352, 2007.
DOI : 10.1016/j.neunet.2007.04.016

E. Johansson, F. Dowla, and D. Goodman, BACKPROPAGATION LEARNING FOR MULTILAYER FEED-FORWARD NEURAL NETWORKS USING THE CONJUGATE GRADIENT METHOD, International Journal of Neural Systems, vol.02, issue.04, pp.291-301, 1991.
DOI : 10.1142/S0129065791000261

J. T. Jolliffe, Principal component analysis, 2004.
DOI : 10.1007/978-1-4757-1904-8

B. Kedem and K. Fokianos, Regression models for time series analysis, 2002.
DOI : 10.1002/0471266981

K. Liang and S. L. Zeger, A Class of Logistic Regression Models for Multivariate Binary Time Series, Journal of the American Statistical Association, vol.40, issue.406, pp.447-451, 1989.
DOI : 10.1080/01621459.1989.10478789

X. Lin, Z. Yang, and Y. Song, Short-term stock price prediction based on echo state networks, Expert Systems with Applications, vol.36, issue.3, pp.7313-7317, 2009.
DOI : 10.1016/j.eswa.2008.09.049

M. Lukosevicius, A Practical Guide to Applying Echo State Networks, Lecture Notes in Computer Science, vol.1, issue.10, pp.659-686, 2012.
DOI : 10.1162/neco.1989.1.2.270

I. L. Macdonald and W. Zucchini, Hidden markov and other models for discrete-valued time series, 1997.

D. P. Mandic and J. A. Chambers, Recurrent neural networks for prediction learning algorithms, architectures and stability, 2001.

P. Mccullagh and J. A. Nelder, Generalized linear models, second [repr, 1989.

P. Pai, System reliability forecasting by support vector machines with genetic algorithms, Mathematical and Computer Modelling, vol.43, issue.3-4, pp.3-4, 2006.
DOI : 10.1016/j.mcm.2005.02.008

R. Salakhutdinov, A. Mnih, and G. Hinton, Restricted Boltzmann machines for collaborative filtering, Proceedings of the 24th international conference on Machine learning, ICML '07, 2007.
DOI : 10.1145/1273496.1273596

G. W. Taylor and G. E. Hinton, Factored conditional restricted boltzmann machines for modeling motion style. Vachtsevanos, G., 2006. Intelligent fault diagnosis and prognosis for engineering systems, 2009.

D. Verstraeten, Reservoir computing: computation with dynamical systems, 2009.

K. Xu, M. Xie, L. C. Tang, and S. L. Ho, Application of neural networks in forecasting engine systems reliability, Applied Soft Computing, vol.2, issue.4, pp.255-268, 2003.
DOI : 10.1016/S1568-4946(02)00059-5

A. Yadav, D. Mishra, R. N. Yadav, S. Ray, and P. K. Kalra, Time-series prediction with single integrate-and-fire neuron, Applied Soft Computing, vol.7, issue.3, pp.739-745, 2007.
DOI : 10.1016/j.asoc.2006.02.004

Z. Zainuddin and O. Pauline, Modified wavelet neural network in function approximation and its application in prediction of time-series pollution data, Applied Soft Computing, vol.11, issue.8, pp.4866-4874, 2011.
DOI : 10.1016/j.asoc.2011.06.013

M. Zeiler, G. Taylor, N. Troje, and G. Hinton, Modeling pigeon behaviour using a conditional restricted boltzmann machine, 17th European Symposium on Artificial Neural Networks (ESANN), 2009.

Y. Zhang and X. Wan, Statistical fuzzy interval neural networks for currency exchange rate time series prediction, Applied Soft Computing, vol.7, issue.4, pp.1149-1156, 2007.
DOI : 10.1016/j.asoc.2006.01.002