title: [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].
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.
authors: Chengdu University of Technology, Chengdu 610059, China. lifeimvp@sina.com
authors: Fei
authors: F
authors: Li
authors: Liang-quan
authors: LQ
authors: Ge
authors: Qing-xian
authors: QX
authors: Zhang
authors: Yi
authors: Y
authors: Gu
authors: Zhi-xiong
authors: ZX
authors: Wan
authors: Wang-yan
authors: WY
authors: Li