What size net gives valid generalization?
Neural Computation
Neural network design
Computing confidence intervals for stochastic simulation using neural network metamodels
Computers and Industrial Engineering - Special issue on computational intelligence for industrial engineering
Neural network-based simulation metamodels for predicting probability distributions
Computers and Industrial Engineering
A neural-network-based nonlinear metamodeling approach to financial time series forecasting
Applied Soft Computing
Neural network topology optimization
ICANN'05 Proceedings of the 15th international conference on Artificial neural networks: formal models and their applications - Volume Part II
Mathematical and Computer Modelling: An International Journal
Confidence interval prediction for neural network models
IEEE Transactions on Neural Networks
Confidence estimation methods for neural networks: a practical comparison
IEEE Transactions on Neural Networks
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Finding optimal structures for neural networks is remains an open problem, despite the rich array of literature on the application of neural networks in different areas of science and engineering. The stochastic nature of operations common in complex systems makes point prediction performance of neural network metamodels an additional challenge. We propose a method for selecting the best structure of a neural network metamodel. For selecting the network structure, the new method uses interval prediction capability of neural networks and chooses a topology that yields the narrowest prediction band for targets. This is an improvement on traditional criteria, such as mean square error or mean absolute percentage error. As a case study, the interval prediction method is applied to a metamodel of a complex system composed of many inextricably interconnected entities and stochastic processes. The demonstrated results expressly show that selecting the network structure based on the proposed method yields more reliable estimates.