连续小波变化-支持向量回归模型及其在谷物近红外光谱分析中的应用(英文)  

CWT-SVR model and its application in NIR analysis of corn

作　　者：刘解放[1] 高普梅[2] 姚树文[3] 

机构地区：[1]河南科技学院数学系,中国新乡453003 [2]河南科技学院新科学院,中国新乡453003 [3]河南科技学院化学化工学院,中国新乡453003

出　　处：《西南民族大学学报（自然科学版）》2008年第5期912-916,共5页Journal of Southwest Minzu University(Natural Science Edition)

基　　金：Henan province science and technology development planproject 0624420016;Henan province education department science and technology attack planproject 007150018

摘　　要：近红外光谱技术是一种简单,快速,无损,价格低廉的方法,可以进行多组分同时分析.支持向量机基于结构风险最小化原理替代了传统方法中的的经验风险最小化原理,使得它具有更好的泛化能力,在许多领域中的应用取得了成功.在这篇文章中,我们把连续小波变化技术结合支持向量机用于近红外光谱分析,结果显示,连续小波变化-支持向量机模型具有更好的预测精度.Near-infrared spectroscopy （NIR） analytical technique is simple, fast and low cost, making neither pollution nor damage to the samples, and can determine many components simultaneously. Support vector machine （SVM） is based on the principle of structural risk minimization, which makes SVM a better generalization ability than other traditional learning machines that are based on the learning principle of empirical risk minimization. It is successful in many fields. In this paper, continuous wave transform （CWT） combined with SVM is used in NIR analysis. Compared with Partial Least Squares （PLS） and support vector regression （SVR）, it shows that the CWT-SVR model has better forecast accuracy.

关 键 词：支持向量回归 近红外光谱 连续小波变换 

分 类 号：O65[理学—分析化学]