| 光谱学与光谱分析 |
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| Research on the Application of Improved M-P Neural Network to the Determination of Lead and Zinc Ore Element Contents by Energy Disperse X-Ray Fluorescence Analysis |
| LI Fei, GE Liang-quan*, ZHANG Qing-xian, GU Yi, WAN Zhi-xiong, LI Wang-yan |
| Chengdu University of Technology, Chengdu 610059, China |
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Abstract Because of different constraints (such as different kinds of measurable elements, characteristic X-ray energy, changes in matrix composition, etc.), usually it’s not easy to get accurate information of elements, resulting in mistakes in later data analysis of energy disperse X-ray fluorescence measurement. The method is based on McCulloch-Pitts neural network (M-P neural network), according to matrix effect, to establish a new neural network model for quantitative forecasting of Zn by taking the data of X-ray fluorescence measurements of Cu, Fe, Pb, etc in lead-zinc mine in western Tianshan as the training sample. The relative error between predicted value and measured value is less than 5%. This method can be more accurate and rapid for X-ray fluorescence; it provides a new approach to correcting information of X-ray fluorescence.
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Received: 2011-10-27
Accepted: 2012-01-12
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Corresponding Authors:
GE Liang-quan
E-mail: glq@cdut.edu.cn
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