Add to ORKGInternational audienceIn this work, we implement a multi-objective genetic algorithm (namely, non-dominated sorting genetic algorithm-II (NSGA-II)) to find the parameters of an ANN which provides in output the lower and upper bounds of the prediction intervals of a quantity of interest. We apply the proposed method on a synthetic case study of literature
Neural networks (NNs) are an effective tool to model nonlinear systems. However, their forecasting p...
Prediction intervals (PIs) are excellent tools for quantification of uncertainties associated with p...
parameters design for full-automation ability is an extremely important task, therefore it is challe...
International audienceIn this work, we implement a multi-objective genetic algorithm (namely, non-do...
The delta technique has been proposed in literature for constructingprediction intervals for targets...
This thesis makes contributions to basic and fundamental research in the field of prediction interva...
Abstract—We consider the task of performing prediction with neural networks on the basis of uncertai...
International audienceThis paper deals with methods for finding the suitable weights in an Artificia...
In this paper, we address multi-step ahead time series Prediction Intervals (PI). We extend two Neur...
Regularization is an essential technique to improve generalization of neural networks. Traditionally...
Abstract: Neural networks are a consistent example of non-parametric estimation, with powerful unive...
This paper aims at developing a new criterion for quantitative assessment of prediction intervals. T...
In this paper we investigate the effective design of an appropriate neural network model for time se...
Neural networks can be viewed as nonlinear models, where the weights are parameters to be estimated....
The transportation literature is rich in the application of neural networks for travel time predict...
Neural networks (NNs) are an effective tool to model nonlinear systems. However, their forecasting p...
Prediction intervals (PIs) are excellent tools for quantification of uncertainties associated with p...
parameters design for full-automation ability is an extremely important task, therefore it is challe...
International audienceIn this work, we implement a multi-objective genetic algorithm (namely, non-do...
The delta technique has been proposed in literature for constructingprediction intervals for targets...
This thesis makes contributions to basic and fundamental research in the field of prediction interva...
Abstract—We consider the task of performing prediction with neural networks on the basis of uncertai...
International audienceThis paper deals with methods for finding the suitable weights in an Artificia...
In this paper, we address multi-step ahead time series Prediction Intervals (PI). We extend two Neur...
Regularization is an essential technique to improve generalization of neural networks. Traditionally...
Abstract: Neural networks are a consistent example of non-parametric estimation, with powerful unive...
This paper aims at developing a new criterion for quantitative assessment of prediction intervals. T...
In this paper we investigate the effective design of an appropriate neural network model for time se...
Neural networks can be viewed as nonlinear models, where the weights are parameters to be estimated....
The transportation literature is rich in the application of neural networks for travel time predict...
Neural networks (NNs) are an effective tool to model nonlinear systems. However, their forecasting p...
Prediction intervals (PIs) are excellent tools for quantification of uncertainties associated with p...
parameters design for full-automation ability is an extremely important task, therefore it is challe...