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

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。

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

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.