Application of artificial neural networks to X‐ray fluorescence spectrum analysis. (27th December 2018)

• Record Type: Journal Article
• Authors: Li, Fei; Gu, Zhixing; Ge, Liangquan; Sun, Di; Deng, Xutao; Wang, Shun; Hu, Bo; Xu, Jingru
• Is Part Of: X-ray spectrometry. Volume 48:Number 2(2019)
• Page Start: 138
• Page End: 150
• Publication Date: 2018-12-27
• Subjects: X-ray spectroscopy -- Periodicals; 543.62
• DOI: 10.1002/xrs.2996
• Languages: English
• ISSNs: 0049-8246

Abstract

Abstract : X‐ray fluorescence (XRF) is widely applied as a mature nondestructive testing method, and appropriate improvement of quantitative analysis methods can improve the accuracy of XRF. Artificial neural network is an intelligent information processing system, its developments and application in XRF are reviewed, and representative models (back propagation, radial basis function, genetic algorithm artificial neural network, and others) are discussed in more details in overfitting, generalization, and algorithm efficiency. Potential directions of developing artificial neural network applied in XRF are proposed in this review as a further study.