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Application of artificial neural networks to X‐ray fluorescence spectrum analysis
Author(s) -
Li Fei,
Gu Zhixing,
Ge Liangquan,
Sun Di,
Deng Xutao,
Wang Shun,
Hu Bo,
Xu Jingru
Publication year - 2018
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.2996
Subject(s) - overfitting , artificial neural network , computer science , artificial intelligence , generalization , genetic algorithm , backpropagation , pattern recognition (psychology) , machine learning , algorithm , mathematics , mathematical analysis
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.