论文信息 - CONSTRUCTION OF CONFIDENCE INTERVALS IN NEURAL MODELING USING A LINEAR TAYLOR EXPANSION

CONSTRUCTION OF CONFIDENCE INTERVALS IN NEURAL MODELING USING A LINEAR TAYLOR EXPANSION

We introduce the theoretical results on the construction of confidence intervals for a nonlinear regression, based on the linear Taylor expansion of the corresponding nonlinear model output. The case of neural black-box modeling is then analyzed, and illustrated on an industrial application. We show that the linear Taylor expansion not only provides a confidence interval at any point of interest, but also gives a tool to detect overfitting.

Léon Personnaz | Isabelle Rivals

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