| 光谱学与光谱分析 |
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| 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 |
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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.
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Received: 2012-07-23
Accepted: 2012-10-25
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Corresponding Authors:
GE Liang-quan
E-mail: glq@cdut.edu.cn
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