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Fit of EDXRF spectra with a genetic algorithm
Author(s) -
Brunetti Antonio,
Golosio Bruno
Publication year - 2001
Publication title -
x‐ray spectrometry
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.447
H-Index - 45
eISSN - 1097-4539
pISSN - 0049-8246
DOI - 10.1002/xrs.464
Subject(s) - levenberg–marquardt algorithm , convergence (economics) , set (abstract data type) , algorithm , task (project management) , genetic algorithm , computer science , energy (signal processing) , spectrum (functional analysis) , mathematical optimization , artificial neural network , mathematics , artificial intelligence , statistics , physics , engineering , systems engineering , quantum mechanics , economics , programming language , economic growth
In energy‐dispersive x‐ray fluorescence analysis, the estimation of the net area of the peaks is a primary requirement. This task requires a non‐linear fitting of the peaks. The most common procedures are based on the Marquardt– Levenberg technique. This technique generally works well only when the spectrum is perfectly known, i.e. all the peaks are recognized. Moreover, it is sometimes difficult to introduce constraints on the fit due to peak shape or other physical properties. In this paper a new technique is proposed, based on a set of genetic operators. It works well even when the knowledge of the peaks is incomplete and it also allows one easily to introduce constraints. The results obtained with this algorithm are generally superior with respect to a standard implementation of a Marquardt– Levenberg procedure. The only drawback is the speed of convergence, that is slower than in the Marquardt– Levenberg technique. Copyright © 2001 John Wiley & Sons, Ltd.