Research on the Application of Principal Component Analysis and Improved BP Neural Network to the Determination of Fe and Ti Contents in Geological Samples
XU Li-peng, GE Liang-quan*, GU Yi, LIU Min, ZHANG Qing-xian, LI Fei, LUO Bin
Chengdu University of Technology, Chengdu 610059, China
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.
徐立鹏，葛良全*，谷 懿，刘 敏，张庆贤，李 飞，罗 斌 . 基于PCA-BP神经网络的EDXRF分析测定地质样品中铁、钛元素含量的应用研究 [J]. 光谱学与光谱分析, 2013, 33(05): 1392-1396.
XU Li-peng, GE Liang-quan*, GU Yi, LIU Min, ZHANG Qing-xian, LI Fei, LUO Bin . Research on the Application of Principal Component Analysis and Improved BP Neural Network to the Determination of Fe and Ti Contents in Geological Samples. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2013, 33(05): 1392-1396.
