Artificial neural networks as a multivariate calibration tool: modelling the Fe-Cr-Ni system in X-ray fluorescence spectroscopy

Bos, A. and Bos, M. and Linden van der, W.E. (1993) Artificial neural networks as a multivariate calibration tool: modelling the Fe-Cr-Ni system in X-ray fluorescence spectroscopy. Analytica Chimica Acta, 277 (2). pp. 289-295. ISSN 0003-2670

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Abstract:	The performance of artificial neural networks (ANNs) for modeling the Cr---Ni---Fe system in quantitative x-ray fluorescence spectroscopy was compared with the classical Rasberry-Heinrich model and a previously published method applying the linear learning machine in combination with singular value decomposition. Apart from determining if ANNs were capable of modeling the desired non-linear relationships, also the effects of using non-ideal and noisy data were studied. For this goal, more than a hundred steel samples with large variations in composition were measured at their primary and secondary K¿ and Kß lines. The optimal calibration parameters for the Rasberry-Heinrich model were found from this dataset by use of a genetic algorithm. ANNs were found to be robust and to perform generally better than the other two methods in calibrating over large ranges.
Item Type:	Article
Copyright:	© 1993 Elsevier Science
Research Group:	Chemical Analysis (CA)
Link to this item:	http://purl.utwente.nl/publications/12461
Official URL:	http://dx.doi.org/10.1016/0003-2670(93)80441-M
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