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

J. Chen, C. Roberts, and P. Weston, Fault detection and diagnosis for railway track circuits using neuro-fuzzy systems, Control Engineering Practice, vol.16, issue.5, pp.585-596, 2008.
DOI : 10.1016/j.conengprac.2007.06.007

B. Efron and R. Tibshirani, An introduction to the bootstrap, 1993.
DOI : 10.1007/978-1-4899-4541-9

O. F. Eker, F. Camci, A. Guclu, H. Yilboga, M. Sevkli et al., A Simple State-Based Prognostic Model for Railway Turnout Systems, IEEE Transactions on Industrial Electronics, vol.58, issue.5, pp.1718-1726, 2011.
DOI : 10.1109/TIE.2010.2051399

J. Han, M. Kamber, and J. Pei, Data mining concepts and techniques, 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

G. Huang and L. Chen, Convex incremental extreme learning machine, Neurocomputing, vol.70, issue.16-18, pp.3056-3062, 1618.
DOI : 10.1016/j.neucom.2007.02.009

G. Huang and L. Chen, Enhanced random search based incremental extreme learning machine, Neurocomputing, vol.71, issue.16-18, pp.3460-3468, 1618.
DOI : 10.1016/j.neucom.2007.10.008

G. Huang, L. Chen, and C. Siew, Universal Approximation Using Incremental Constructive Feedforward Networks With Random Hidden Nodes, IEEE Transactions on Neural Networks, vol.17, issue.4, pp.879-892, 2006.
DOI : 10.1109/TNN.2006.875977

G. Huang, D. Wang, and Y. Lan, Extreme learning machines: a survey, International Journal of Machine Learning and Cybernetics, vol.23, issue.3, pp.107-122, 2011.
DOI : 10.1007/s13042-011-0019-y

G. Huang, H. Zhou, X. Ding, and R. Zhang, Extreme Learning Machine for Regression and Multiclass Classification, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), vol.42, issue.2, pp.513-529, 2012.
DOI : 10.1109/TSMCB.2011.2168604

G. Huang, Q. Zhu, and C. Siew, Extreme learning machine: a new learning scheme of feedforward neural networks, Proceedings. 2004 IEEE International Joint Conference on, pp.985-990, 2004.

G. Huang, Q. Zhu, and C. Siew, Extreme learning machine: Theory and applications, Neurocomputing, vol.70, issue.1-3, pp.489-501, 2006.
DOI : 10.1016/j.neucom.2005.12.126

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.217.3692

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

S. Lolas and O. A. Olatunbosun, Prediction of vehicle reliability performance using artificial neural networks, Expert Systems with Applications, vol.34, issue.4, pp.2360-2369, 2008.
DOI : 10.1016/j.eswa.2007.03.014

F. P. Marquez, P. Weston, and C. Roberts, Failure analysis and diagnostics for railway trackside equipment, Engineering Failure Analysis, vol.14, issue.8, pp.1411-1426, 2007.
DOI : 10.1016/j.engfailanal.2007.03.005

M. D. Moura, E. Zio, I. D. Lins, and E. Droguett, Failure and reliability prediction by support vector machines regression of time series data, Reliability Engineering & System Safety, vol.96, issue.11, pp.1527-1534, 2011.
DOI : 10.1016/j.ress.2011.06.006

C. R. Rao and S. K. Mitra, Generalized inverse of matrices and its applications, 1971.

G. Vachtsevanos, Intelligent fault diagnosis and prognosis for engineering systems, 2006.
DOI : 10.1002/9780470117842

V. N. Vapnik, Estimation of dependences based on empirical data reprint of, 1982.

H. Yilboga, O. F. Eker, A. Guclu, and F. Camci, Failure prediction on railway turnouts using time delay neural networks, 2010 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications, pp.134-137, 2010.
DOI : 10.1109/CIMSA.2010.5611756

K. Zhang, Y. Li, P. Scarf, and A. Ball, Feature selection for high-dimensional machinery fault diagnosis data using multiple models and Radial Basis Function networks, Neurocomputing, vol.74, issue.17, pp.2941-2952, 2011.
DOI : 10.1016/j.neucom.2011.03.043