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化学学报
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化学学报 ›› 2009, Vol. 67 ›› Issue (10): 1081-1086. 上一篇    下一篇

研究论文

加权最小二乘支持向量机稳健化迭代算法及其在光谱分析中的应用

包 鑫 戴连奎

  

  1. (浙江大学工业控制技术国家重点实验室 杭州 310027)
  • 投稿日期:2008-07-30 修回日期:2008-12-30 发布日期:2009-05-28
  • 通讯作者: 戴连奎

Robust Iterative Algorithm of Weighted Least Squares Support Vector Machine and Its Application in Spectral Analysis

Bao, Xin Dai, Liankui*   

  1. (State Key Laboratory of Industrial Control Technology, Zhejiang University, Hangzhou 310027)
  • Received:2008-07-30 Revised:2008-12-30 Published:2009-05-28
  • Contact: Dai, Liankui

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被引次数

21 | 4

为克服光谱分析中异常训练样本的影响, 提出了一种加权最小二乘支持向量机(WLS-SVM)的稳健化迭代算法. 针对原始WLS-SVM在收敛性和稳健性方面的不足, 提出了一种新的求取回归误差的方法, 从而从根本上解决了WLS-SVM的收敛性问题; 同时对原始算法求权值的步骤进行了修正, 采用回归误差的中值作为计算加权值的比较基准, 大幅度提高了WLS-SVM的稳健性. 将算法应用于光谱定量分析中, 实验结果证明了该方法是收敛的, 并且崩溃点在35%左右, 是一种有效的稳健建模方法.

关键词: 光谱分析, 最小二乘支持向量机, 收敛性, 稳健性, 崩溃点

A robust iterative algorithm of weighted least squares support vector machine (WLS-SVM) was proposed to overcome the negative influence of outliers on spectral analysis. A novel method to calculate regression error was proposed to solve the iterative convergence problem in original WLS-SVM; to improve the robustness of original WLS-SVM, the formula for computing weighted value was also revised: the median value of regression error was selected as criteria. This algorithm has been applied to spectral quantitative analysis. Compared with original algorithm, the experimental results show the proposed algorithm is convergent and the breakdown point value is approximately 35%.

Key words: spectral analysis, least squares support vector machine, convergence, robustness, breakdown point