Artificial neural networks as a multivariate calibration tool: modeling the FeCrNi system in x-ray fluorescence spectroscopy
✍ Scribed by A. Bos; M. Bos; W.E. van der Linden
- Publisher
- Elsevier Science
- Year
- 1993
- Tongue
- English
- Weight
- 657 KB
- Volume
- 277
- Category
- Article
- ISSN
- 0003-2670
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✦ Synopsis
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-Heimich model and a previously published method applying the linear learning machine in combination with singular value decomposition. Apart from determining lf 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 KS 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.