Neural Networks - 2006 special issue: Earth sciences and environmental applications of computational intelligence
Neural network-based simulation metamodels for predicting probability distributions
Computers and Industrial Engineering
Prediction intervals for neural network models
ICCOMP'05 Proceedings of the 9th WSEAS International Conference on Computers
Load curve estimation for distribution systems using ANN
CIMMACS'08 Proceedings of the 7th WSEAS international conference on Computational intelligence, man-machine systems and cybernetics
Robust Estimation of Confidence Interval in Neural Networks applied to Time Series
IWANN '03 Proceedings of the 7th International Work-Conference on Artificial and Natural Neural Networks: Part II: Artificial Neural Nets Problem Solving Methods
Efficient Monte Carlo computation of Fisher information matrix using prior information
Computational Statistics & Data Analysis
A prediction interval-based approach to determine optimal structures of neural network metamodels
Expert Systems with Applications: An International Journal
Constructing prediction intervals for neural network metamodels of complex systems
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Developing optimal neural network metamodels based on prediction intervals
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Efficient confidence bounds for RBF networks for sparse and high dimensional data
ICANN'10 Proceedings of the 20th international conference on Artificial neural networks: Part III
A prediction interval estimation method for KMSE
ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part I
Customized prediction of respiratory motion with clustering from multiple patient interaction
ACM Transactions on Intelligent Systems and Technology (TIST) - Survey papers, special sections on the semantic adaptive social web, intelligent systems for health informatics, regular papers
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To derive an estimate of a neural network's accuracy as an empirical modeling tool, a method to quantify the confidence intervals of a neural network model of a physical system is desired. In general, a model of a physical system has error associated with its predictions due to the dependence of the physical system's output on uncontrollable or unobservable quantities. A confidence interval can be computed for a neural network model with the assumption of normally distributed error for the neural network. The proposed method accounts for the accuracy of the data with which the neural network model is trained