摘要
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.
rt 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%.
出处
《化学学报》
SCIE
CAS
CSCD
北大核心
2009年第10期1081-1086,共6页
Acta Chimica Sinica
基金
国家863计划(No.2006AA04Z169)资助项目
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
spectral analysis
least squares support vector machine
convergence
robustness
breakdown point
