title: Confidence intervals for calibration with neural networks.
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.
authors.affiliation: TU Bergakademie Freiberg, Institute for Analytical Chemistry, Leipziger Strasse 29, D-09599, Freiberg/Sa, Germany.
authors.forename: M
authors.initials: M
authors.lastname: Dathe
authors.forename: M
authors.initials: M
authors.lastname: Otto