基于粒子群优化支持向量机的LIBS钢液Mn元素定量分析  

Quantitative Analysis of Mn Element in Liquid Steel by LIBS Based on Particle Swarm Optimized Support Vector Machine

作　　者：杨友良[1] 王鹏[1] 马翠红[1] 

机构地区：[1]河北联合大学电气工程学院,河北唐山063000

出　　处：《激光与光电子学进展》2015年第7期283-288,共6页Laser & Optoelectronics Progress

基　　金：国家自然科学基金(61271402)

摘　　要：为了更好地监测钢液成分,利用激光诱导击穿光谱(LIBS)技术,建立了基于径向基函数(RBF)核函数的支持向量机模型,采用粒子群算法优化支持向量机的参数,通过计算元素特征谱线的积分强度和Fe元素内标归一化来降低仪器和环境带来的干扰。将实验数据进行主成分降维后,对钢液中Mn元素的浓度进行定量分析,得到均方根误差(MSE)为0.599%,相对标准偏差(RSD)为8.26%,相关系数为0.997。结果显示,粒子群优化支持向量机回归定量分析方法可以用于LIBS钢液成分分析,其分析性能较传统的定标方法有一定提高。In order to make better use of laser induced breakdown spectroscopy （LIBS） in the liquid steel composition monitoring, a model of support vector machine based on radial basis function （RBF） kernel function is established by using particle swarm optimized support vector machine. In order to reduce the interference of instrument and environment, integral intensity of spectral line and Fe normalization is used. The experimental data is subjected to principal component analysis to carry out quantitative analysis of the concentration of Mn element in molten steel, it is obtained that the mean square error （MSE） is 0.599%, the relative standard deviation （RSD） is 8.26%, the correlation coefficient is 0.997. The results show that the particle swarm optimized support vector machine regression method can be used to analyze LIBS of liquid steel composition, its analytical performance is improved compared with traditional calibration methods.

关 键 词：光谱学 激光诱导击穿光谱 支持向量机 定量分析 

分 类 号：TN247[电子电信—物理电子学]