Modeling with constructive backpropagation
Neural Networks
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
A prediction interval-based approach to determine optimal structures of neural network metamodels
Expert Systems with Applications: An International Journal
COVNET: a cooperative coevolutionary model for evolving artificial neural networks
IEEE Transactions on Neural Networks
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This paper studies how an optimal Neural Network (NN) can be selected that is later used for constructing the highest quality delta-based Prediction Intervals (PIs). It is argued that traditional assessment criteria, including RMSE, MAPE, BIC, and AIC, are not the most appropriate tools for selecting NNs from a PI-based perspective. A new NN model selection criterion is proposed using the specific features of the delta method. Using two synthetic and real case studies, it is demonstrated that this criterion outperforms all traditional model selection criteria in terms of picking the most appropriate NN. NNs selected using this criterion generate high quality PIs evaluated by their length and coverage probability.