改进型M-P神经网络在能量色散X荧光分析测定铅锌矿元素含量的应用研究

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

以新疆西天山铅锌矿样品的Cu,Fe,Pb等元素X荧光测量数据做训练样本,McCulloch-Pitts神经网络(M-P神经网络)为基础,基体效应为依据,建立新的神经网络模型对Zn进行定量预测。结果预测值与测量值的相对误差在<5%。此方法可较准确,快速的应用于现场X荧光测定,为X荧光光谱信息修正提供一种新方法。

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.