光谱学与光谱分析, 2013, 33 (5): 1392, 网络出版: 2013-05-21  

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

Research on the Application of Principal Component Analysis and Improved BP Neural Network to the Determination of Fe and Ti Contents in Geological Samples
作者单位
成都理工大学, 四川 成都610059
摘要
为实现地质样品中元素含量的准确预测, 提出了基于主成分分析(PCA)的改进型BP神经网络模型。 采用X荧光光谱法, 对新疆西天山地质样品中Fe, Ti, V, Pb和Zn等元素进行测量, 将得到的X荧光计数作为输入变量, 应用该模型对未知地质样品中Fe和Ti元素进行定量预测。 结果表明: 主成分分析与改进型BP神经网络模型取得了较好的预测效果, 预测结果与化学分析值的相对误差小于3%, 为地质样品元素含量预测提供了一种新型有效的方法。
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(5): 1392. 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[J]. Spectroscopy and Spectral Analysis, 2013, 33(5): 1392.

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