大气与环境光学学报, 2012, 7 (2): 124, 网络出版: 2012-03-30   

神经网络在测定炉渣中Ca和Mg含量的应用

Quantitative Analysis of Ca and Mg in Slag with Artificial Neural Networks
作者单位
1 中国科学院安徽光学精密机械研究所, 安徽合肥?230031
2 合肥工业大学仪器科学与光电工程学院,安徽 合肥?230009
摘要
为对炉渣中的Ca、Mg含量进行定量分析,将反向传播神经网络与激光诱导击穿光谱技术相结合,采用自适应 学习速率结合附加动量的方法对25种样品进行网络仿真训练,建立了定标模型。鉴于网络输入对提高测量结 果重复性和准确性的影响,训练过程中着重研究了仅使用元素谱线积分强度及将一段背景谱线强度与元素谱 线积分强度相结合的两种网络输入对网络性能的影响,并在非训练样品中任意抽取5种样品,对定标模型进 行了验证。结果表明,在分析成分复杂的炉渣中的Ca、Mg含量时,采用加入一段背景谱线积分强度的网络输 入,神经网络能够更充分的利用光谱中的信息,对消除基体效应和谱线之间的干扰具有较好的预测效果。
Abstract
Back-propagation neural network combining with laser-induced breakdown spectroscopy (LIBS) are used to calibrate and quantify the contents of Ca and Mg of different kinds of slag. The networks were trained by means of a gradient descent with momentum and adaptive learning rate back-propagation algorithm. The performance of the neural networks with different inputs is studied, so as its predictive performances to be improved, and the effect of the presence of matrix-specific information in the inputs was studied. Higher performance was obtained when the network was fed with one matrix-specific spectral window than only with the areas of selected peaks. The network fed with one matrix-specific spectral window can utilize more information of spectra, and better correct the matrix effect and line interference. The inputs of the neural networks, however, need serious consideration, since they have a good effect on the measurement reproducibility and accuracy.

梁云仙, 陈兴龙, 王琦, 王静鸽, 杨阳, 倪志波, 董凤忠. 神经网络在测定炉渣中Ca和Mg含量的应用[J]. 大气与环境光学学报, 2012, 7(2): 124. LIANG Yun-xian, CHEN Xing-long, WANG Qi, WANG Jing-ge, YANG Yang, NI Zhi-bo, DONG Feng-zhong. Quantitative Analysis of Ca and Mg in Slag with Artificial Neural Networks[J]. Journal of Atmospheric and Environmental Optics, 2012, 7(2): 124.

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