摘要
为了提高近红外光谱分析精度,提出结合偏最小二乘判别分析(PLS-DA)与支持向量机(SVM)的软测量方法(PLS-DA-SVM).该方法利用一组由不同类别组成的训练样本,引入二叉树进行多重分类,节点分类器由PLS-DA方法建立;利用偏最小二乘支持向量机(PLS-SVM)建立每类样本的定量模型.预测时,用PLS-DA分类树对待测样本进行分类,选择相应的PLS-SVM模型进行定量分析.实验利用PLS-DA-SVM方法和近红外光谱数据建立汽油的研究法辛烷值软测量模型,针对2个批次共计57个成品汽油样本进行蒙特卡洛交叉检验.结果表明,对汽油牌号进行识别,平均分类错误率为0.07%,低于其他常用分类方法;对研究法辛烷值进行预测,均方误差达到0.243,复相关系数达到0.991,较PLS、LS-SVM等方法有显著提高.
To improve the performance of near-infrared spectral analysis, this paper proposes a soft-sensing method （PLS-DA-SVM） which combines partial least squares discriminant analysis （PLS-DA） and support vector machine （SVM）. Based on training samples with several classes, firstly, a binary tree built by PLSDA is introduced for multiple classification; secondly, sub-models for quantitative analy are constructed by partial least squares support vector machine （PLS-SVM）. For a test sample, PLS-DA classification tree serves to determine its class, and the corresponding PLS-SVM sub-model is selected for quantitative analysis. A PLS-DA-SVM model with near-infrared spectroscopy data was established to determine the research octane number of gasoline samples. Monte Carlo cross validation was preformed with 57 product gasoline samples from 2 oil refineries. Results show that mean classification error rate for the recognition of gasoline brands is 0.07 %, which is lower than other pattern recognition methods. Root mean square error of prediction （RMSEP） is reduced to 0. 243 and correlation coefficient （R^2） is up to 0. 991, which show great improvement upon PLS, LS-SVM and other modeling methods.
出处
《浙江大学学报（工学版）》
EI
CAS
CSCD
北大核心
2012年第5期824-829,共6页
Journal of Zhejiang University：Engineering Science
基金
国家"863"高技术研究发展计划资助项目(2009AA04Z123)
关键词
软测量
近红外光谱
偏最小二乘
支持向量机
soft-sensing
near-infrared spectroscopy
partial least squares
support vector machine
