Nuclear Science and Techniques

《核技术》(英文版) ISSN 1001-8042 CN 31-1559/TL     2019 Impact factor 1.556

Nuclear Science and Techniques ›› 2014, Vol. 25 ›› Issue (3): 030203 doi: 10.13538/j.1001-8042/nst.25.030203

• LOW ENERGY ACCELERATOR, RAY AND APPLICATIONS • Previous Articles     Next Articles

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

WANG Jun, LIU Ming-Zhe, TUO Xian-Guo, LI Zhe, LI Lei, SHI Rui   

  1. 1State Key Laboratory of Geohazard Prevention and Geoenvironment Protection (Chengdu University of Technology), Chengdu 610059, China
    2Key Subject Laboratory of National Defense for Radioactive Waste and Environmental Security, Southwest University of Science and Technology, Mianyang 621010, China
  • Contact: LIU Ming-Zhe
  • Supported by:

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

WANG Jun, LIU Ming-Zhe, TUO Xian-Guo, LI Zhe, LI Lei, SHI Rui. A genetic-algorithm-based neural network approach for EDXRF analysis.Nuclear Science and Techniques, 2014, 25(3): 030203     doi: 10.13538/j.1001-8042/nst.25.030203


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.

Key words: EDXRF, Quantitative analysis, BP neural network, Genetic algorithm