Construction of Confidence Intervals in Neural Modeling Using a Linear Taylor Expansion
نویسنده
چکیده
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.
منابع مشابه
Construction of confidence intervals for neural networks based on least squares estimation
We present the theoretical results about the construction of confidence intervals for a nonlinear regression based on least squares estimation and using the linear Taylor expansion of the nonlinear model output. We stress the assumptions on which these results are based, in order to derive an appropriate methodology for neural black-box modeling; the latter is then analyzed and illustrated on s...
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