title: [Research on the application of principal component analysis and improved BP neural network to the determination of Fe and Ti contents in geological samples].
abstract: Aiming at forecasting elemental contents in geological samples accurately, a principal component analysis and improved BP (PCA-BP) neural network theory is proposed in the present work. The samples from west Tianshan were measured through X-ray fluorescence measurement method, and the X-Ray fluorescence counts of each element such as Fe, Ti, V, Pb, Zn, etc. were input to the PCA-BP neural network as input variables to forecast Fe and Ti contents in uncertified geological samples quantitatively. The results show that the PCA-BP neural network can give an ideal result, and the relative error between the forecast data and chemical analysis data is less than 3%. This method provides a new and effective approach to forecasting elemental contents in geological samples.
authors.affiliation: Chengdu University of Technology, Chengdu 610059, China. xulipeng-cdut@163.com
authors.forename: Li-Peng
authors.initials: LP
authors.lastname: Xu
authors.forename: Liang-Quan
authors.initials: LQ
authors.lastname: Ge
authors.forename: Yi
authors.initials: Y
authors.lastname: Gu
authors.forename: Min
authors.initials: M
authors.lastname: Liu
authors.forename: Qing-Xian
authors.initials: QX
authors.lastname: Zhang
authors.forename: Fei
authors.initials: F
authors.lastname: Li
authors.forename: Bin
authors.initials: B
authors.lastname: Luo