A genetic-algorithm-based neural network approach for EDXRF analysis  

作　　者：王俊 刘明哲 庹先国 李哲 李磊 石睿 

机构地区：[1]State Key Laboratory of Geohazard Prevention and Geoenvironment Protection (Chengdu University of Technology) [2]Key Subject Laboratory of National Defense for Radioactive Waste and Environmental Security,Southwest University of Science and Technology

出　　处：《Nuclear Science and Techniques》2014年第3期18-21,共4页核技术（英文）

基　　金：Supported by National Outstanding Youth Science Foundation of China(No.41025015);the National Natural Science Foundation of China(No.41274109);Sichuan Youth Science and Technology Innovation Research Team(No.2011JTD0013)

摘　　要：In energy dispersive X-ray fiuorescence(EDXRF), quantitative elemental content analysis becomes difficult due to the existence of the noise, the spectrum peak superposition, element matrix effect, etc. In this paper, a hybrid approach of genetic algorithm(GA) and back propagation(BP) neural network is proposed without considering the complex relationship between the elemental content and peak intensity. The aim of GA-optimized BP is to get better network initial weights and thresholds. The starting point of this approach is that the reciprocal of the mean square error of the initialization BP neural network is set as the fitness value of the individuals in GA; and the initial weights and thresholds are replaced by individuals, then the optimal individual is searched by selecting, crossover and mutation operations, finally a new BP neural network model is established with the optimal initial weights and thresholds. The quantitative analysis results of titanium and iron contents in five types of mineral samples show that the relative errors of 76.7% samples are below 2%, compared to chemical analysis data, which demonstrates the effectiveness of the proposed method.In energy dispersive X-ray fluorescence （EDXRF）, quantitative elemental content analysis becomes difficult due to the existence of the noise, the spectrum peak superposition, element matrix effect, etc. In this paper, a hybrid approach of genetic algorithm （GA） and back propagation （BP） neural network is proposed without con- sidering the complex relationship between the elemental content and peak intensity. The aim of GA-optimized BP is to get better network initial weights and thresholds. The starting point of this approach is that the recipro- cal of the mean square error of the initialization BP neural network is set as the fitness value of the individuals in GA; and the initial weights and thresholds are replaced by individuals, then the optimal individual is searched by selecting, crossover and mutation operations, finally a new BP neural network model is established with the optimal initial weights and thresholds. The quantitative analysis results of titanium and iron contents in five types of mineral samples show that the relative errors of 76.7% samples are below 2%, compared to chemical analysis data, which demonstrates the effectiveness of the proposed method.

关 键 词：神经网络方法 遗传算法 XRF分析 基础 初始权值 GA优化 神经网络模型 元素含量 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程]