Multilayer feedforward networks are universal approximators
Neural Networks
Gradient-Based Optimization of Hyperparameters
Neural Computation
Time-series prediction with single integrate-and-fire neuron
Applied Soft Computing
Statistical fuzzy interval neural networks for currency exchange rate time series prediction
Applied Soft Computing
Restricted Boltzmann machines for collaborative filtering
Proceedings of the 24th international conference on Machine learning
Short-term stock price prediction based on echo state networks
Expert Systems with Applications: An International Journal
Factored conditional restricted Boltzmann Machines for modeling motion style
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Expert Systems with Applications: An International Journal
System reliability forecasting by support vector machines with genetic algorithms
Mathematical and Computer Modelling: An International Journal
Learning capability and storage capacity of two-hidden-layer feedforward networks
IEEE Transactions on Neural Networks
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In this paper, a hybrid approach to combine conditional restricted Boltzmann machines (CRBM) and echo state networks (ESN) for binary time series prediction is proposed. Both methods have demonstrated their ability to extract complex dynamic patterns from time-dependent data in several applications and benchmark studies. To the authors' knowledge, it is the first time that the proposed combination of algorithms is applied for reliability prediction. The proposed approach is verified on a case study predicting the occurrence of railway operation disruptions based on discrete-event data, which is represented by a binary time series. The case study concerns speed restrictions affecting railway operations, caused by failures of tilting systems of railway vehicles. The overall prediction accuracy of the algorithm is 99.93%; the prediction accuracy for occurrence of speed restrictions within the foresight period is 98% (which corresponds to the sensitivity of the algorithm). The prediction results of the case study are compared to the prediction with a MLP trained with a Newton conjugate gradient algorithm. The proposed approach proves to be superior to MLP.