Confidence interval prediction for neural network models

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Abstract
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.

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