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Confidence intervals for calibration with neural networks

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Abstract

Based on neural network calibration the confidence intervals of aromaticity determination from infrared reflectance spectra of raw brown coals were estimated by means of the bootstrap method, a simplified Monte Carlo Simulation. The standard deviations and the confidence intervals were estimated to characterise the analysis error. It is shown that confidence intervals of non-linear analysis methods like Back Propagation Neural Networks (BPNN) can be estimated by the bootstrap method. The estimated confidence intervals of the calibration confirm the analysis by BPNN.

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Authors and Affiliations

  1. Institute for Analytical Chemistry, TU Bergakademie Freiberg, Leipziger Strasse 29, D-09599, Freiberg/Sa, Germany

    M. Dathe & M. Otto

Authors
  1. M. Dathe
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  2. M. Otto
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Dathe, M., Otto, M. Confidence intervals for calibration with neural networks. Fresenius J Anal Chem 356, 17–20 (1996). https://doi.org/10.1007/s0021663560017

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  • DOI: https://doi.org/10.1007/s0021663560017

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