Gradient-Based Optimization of Hyperparameters, Neural Computation, vol.58, issue.8, pp.1889-1900, 2000. ,
DOI : 10.1038/317314a0
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
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
An introduction to the bootstrap, 1993. ,
DOI : 10.1007/978-1-4899-4541-9
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
Data mining concepts and techniques, Neural networks and learning machines, 2009. ,
Ridge Regression: Biased Estimation for Nonorthogonal Problems, Technometrics, vol.24, issue.1, pp.55-67, 1970. ,
DOI : 10.2307/1909769
Convex incremental extreme learning machine, Neurocomputing, vol.70, issue.16-18, pp.3056-3062, 1618. ,
DOI : 10.1016/j.neucom.2007.02.009
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
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
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
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
Extreme learning machine: a new learning scheme of feedforward neural networks, Proceedings. 2004 IEEE International Joint Conference on, pp.985-990, 2004. ,
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
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
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
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
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
Generalized inverse of matrices and its applications, 1971. ,
Intelligent fault diagnosis and prognosis for engineering systems, 2006. ,
DOI : 10.1002/9780470117842
Estimation of dependences based on empirical data reprint of, 1982. ,
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
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