What size net gives valid generalization?
Neural Computation
Model selection in neural networks
Neural Networks
Computing confidence intervals for stochastic simulation using neural network metamodels
Computers and Industrial Engineering - Special issue on computational intelligence for industrial engineering
Neural Networks - 2006 special issue: Earth sciences and environmental applications of computational intelligence
A short-term capacity trading method for semiconductor fabs with partnership
Expert Systems with Applications: An International Journal
Enabling industrial scale simulation/emulation models
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Neural network-based simulation metamodels for predicting probability distributions
Computers and Industrial Engineering
Development of metamodeling based optimization system for high nonlinear engineering problems
Advances in Engineering Software
Efficient prediction of exchange rates with low complexity artificial neural network models
Expert Systems with Applications: An International Journal
Process parameter optimization for MIMO plastic injection molding via soft computing
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Estimating performance indexes of a baggage handling system using metamodels
ICIT '09 Proceedings of the 2009 IEEE International Conference on Industrial Technology
Fast bootstrap methodology for regression model selection
Neurocomputing
RSCTC'06 Proceedings of the 5th international conference on Rough Sets and Current Trends in Computing
Making use of population information in evolutionary artificialneural networks
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Mathematical and Computer Modelling: An International Journal
Confidence interval prediction for neural network models
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
Constructive neural-network learning algorithms for pattern classification
IEEE Transactions on Neural Networks
Confidence estimation methods for neural networks: a practical comparison
IEEE Transactions on Neural Networks
COVNET: a cooperative coevolutionary model for evolving artificial neural networks
IEEE Transactions on Neural Networks
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
Expert Systems with Applications: An International Journal
Developing a robust prediction interval based criterion for neural network model selection
ICONIP'10 Proceedings of the 17th international conference on Neural information processing: models and applications - Volume Part II
Engineering Applications of Artificial Intelligence
Multilayer perceptron for simulation models reduction: Application to a sawmill workshop
Engineering Applications of Artificial Intelligence
Expert Systems with Applications: An International Journal
Hi-index | 12.05 |
Neural networks have been widely used in literature for metamodeling of complex systems and often outperform their traditional counterparts such as regression-based techniques. Despite proliferation of their applications, determination of their optimal structure is still a challenge, especially if they are developed for prediction and forecasting purposes. Researchers often seek a tradeoff between estimation accuracy and structure complexity of neural networks in a trial and error basis. On the other hand, the neural network point prediction performance considerably drops as the level of complexity and amount of uncertainty increase in systems that data originates from. Motivated by these trends and drawbacks, this research aims at adopting a technique for constructing prediction intervals for point predictions of neural network metamodels. Space search for neural network structures will be defined and confined based on particular features of prediction intervals. Instead of using traditional selection criteria such as mean square error or mean absolute percentage error, prediction interval coverage probability and normalized mean prediction interval will be used for selecting the optimal network structure. The proposed method will be then applied for metamodeling of a highly detailed discrete event simulation model which is essentially a validated virtual representation of a large real world baggage handling system. Through a more than 77% reduction in number of potential candidates, optimal structure for neural networks is found in a manageable time. According to the demonstrated results, constructed prediction intervals using optimal neural network metamodel have a satisfactory coverage probability of targets with a low mean of length.