Comparison of the training of neural networks for quantitative x-ray fluorescence spectrometry by a genetic algorithm and backward error propagation

Comparison of the quantitative results c

Comparison of the quantitative results corrected by fundamental parameter method and difference calibration specimens in X-ray fluorescence spectrometry

✍ X.Y. Han; S.J. Zhuo; R.X. Shen; P.L. Wang; A. Ji 📂 Article 📅 2006 🏛 Elsevier Science 🌐 English ⚖ 144 KB

Three kinds of standard specimens, including low-alloy steels (Japanese iron and steel CRM), glass from NIST and Chinese geological reference materials were analyzed by wavelength dispersive X-ray fluorescence spectrometry as unknown samples. The fundamental parameter method was used to correct the