Neural network design
Neural Networks - 2006 special issue: Earth sciences and environmental applications of computational intelligence
Enabling industrial scale simulation/emulation models
Proceedings of the 39th conference on Winter simulation: 40 years! The best is yet to come
Development of metamodeling based optimization system for high nonlinear engineering problems
Advances in Engineering Software
A comparison of some error estimates for neural network models
Neural Computation
A neural-network-based nonlinear metamodeling approach to financial time series forecasting
Applied Soft Computing
RSCTC'06 Proceedings of the 5th international conference on Rough Sets and Current Trends in Computing
Mathematical and Computer Modelling: An International Journal
Confidence interval prediction for neural network models
IEEE Transactions on Neural Networks
Improving Prediction Interval Quality: A Genetic Algorithm-Based Method Applied to Neural Networks
ICONIP '09 Proceedings of the 16th International Conference on Neural Information Processing: Part II
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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A rich literature discussing techniques for adopting neural networks for metamodelling of complex systems exists. The main focus in many studies conducted so far has been on training and utilising neural networks as point estimators/predictors. Uncertainties prevailing within complex systems and dependencies amongst constituent entities are real threats for prediction performance of these types of metamodels. From a practical point of view, an indication of prediction accuracy is necessary before making a decision based on results yielded by a metamodel. In this paper we adopt neural network metamodels for constructing prediction intervals of stochastic system performance measures. Upper and lower bounds of a prediction interval are computed such that the real system performance will lie between them with a high probability. Demonstrated results for a real world case study show that the constructed prediction intervals cover the targets, are more informative and more suited for decision making, when compared with point predictions.