摘要
针对传统近红外光谱波长选择方法忽略模型中非线性因素的缺陷,采用具有非线性处理能力的最小二乘支持向量机,结合间隔策略的波长选择方法和联合区间的思想,提出了一种非线性模型下的波长筛选算法—联合区间最小二乘支持向量机(synergy interval least squares support vector machines,siLSSVM)。以苹果糖度近红外光谱数据为例,与传统siPLS波长筛选方法相比,新算法的预测集均方根误差(RMSEP)在PLS模型和LSSVM模型预测时分别提高了37.43%和47.88%,预测集相关系数(RP)在PLS模型和LSSVM模型预测时分别增加了6.04%和7.31%。实例表明,对于存在非线性因素较强的光谱数据,siLSSVM算法能够有效的挑选最优波长区间与提高模型的预测精度和鲁棒性,为近红外光谱在非线性因素下筛选波长提供了新前景。
The present paper proposes a wavelength selection algorithm based on nonlinear factors named Synergy interval least squares support vector machines （siLSSVM ） .siLSSVM combines the interval strategy of wavelength selection method with the idea of synergy interval and overcomes the disadvantages of the traditional wavelength selection methods ,i .e .ignoring the non-linear factors .Taking the near infrared spectrum data of apple sugar as performance verification object of this new algorithm , comparing new algorithm with siPLS ,the model performance has been greatly improved .The root-mean-square error （RMSEP） in new algorithm has increased respectively by 37.43% and 47.88% under the model of PLS and LSSVM ,with increases of 6.04% and 7.31% in the correlative coefficient （RP） .The examples illustrate that siLSSVM can efficiently select the optimum wavelength interval for spectrum data with strong nonlinear factors .This algorithm greatly improves the prediction accuracy and robustness of the model ,which provides a new prospect for near infrared spectral with nonlinear factors to select wavelength .
出处
《光谱学与光谱分析》
SCIE
EI
CAS
CSCD
北大核心
2014年第3期668-672,共5页
Spectroscopy and Spectral Analysis
基金
教育部国家级创新创业训练计划项目(201210295058)
江苏省产学研前瞻性联合研究项目(BY2013015-27)资助
关键词
联合区间最小二乘支持向量机
非线性
苹果糖度
近红外光谱
波长筛选
Synergy interval least squares support vector machines
Nonlinear factors
Data of apple sugar
Near infrared spec-trum
Wavelength selection
作者简介
彭秀辉，1990年生，江南大学自动化研究所本科生e-mail：jnwlpxh@163．com
通讯联系人e-mail：fliu@jiangnan．edu．cn
