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Confidence intervals for calibration with neural networks

Fresenius' Journal of Analytical Chemistry volume 356, pages 17–20 (1996)Cite this article

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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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

Keywords

  • Bootstrap Method
  • Brown Coal
  • Back Propagation Neural Network
  • Estimate Confidence Interval
  • Computing Workload