一种用于可见-近红外光谱特征波长选择的新方法

提出了一种基于模拟退火(SA)算法和最小二乘法支持向量机(LS-SVM)选择可见一近红外光谱特征波长的新方法(SA-LS-SVM).该方法用LS-SVM作为识别器,用识别率作为SA的目标函数,提取合适的特征波长数以及对应的特征波长.3种不同品牌的润滑油可见-近红外光谱的特征波长分别用SA_LS-SVM,主成分回归分析(PCA)和偏最小二乘法(PLS)进行处理,提取特征波长或主成分,然后结合反向传播人工神经网络(BP-ANN)对各种处理方法进行识别预测.结果发现,SA-LS-SVM只需从751个数据光谱中提取4个特征波长,就可以使三种品牌润滑油的识别率达到了100%,而其他所有的方法发现预测率都达不到100%,由此验证了SA_LS-SVM的优越性.实验结果表明,SA-LS-SVM不仅能有效地减少建模的变量数,而且可以提高预测精度.

A New Choice Method of Characteristic Wavelength of Visible/Near Infrared Spectroscopy

A new method based on simulated annealing algorithm (SA) and least-squares support vector machine (LS-SVM) (SA-LS-SVM) was proposed to select the characteristic wavelength for visible-near infrared (Vis/NIR) spectroscopy discrimination. In order to find suitable numbers of characteristic wavelength and corresponding characteristic wavelength, discriminating rate was used as object function for SA, and LS-SVM was adopted as discrimination model. The Vis/NIR spectroscopy characteristic wavelengths of three categories of lubricant were processed by SA-LS-SVM, principal component analysis (PCA) and partial least squares (PLS) respectively, and then predicted by back-propagation artificial neural network (BP-ANN). The results of experiment showed that discriminating rate by using combination of SA-LS-SVM with BP-ANN reaches 100% only using 4 characteristic wavelengths from total of 751 wavelengths, while discriminating rate did not reach 100% by other methods. The proposed algorithm not only reduced the number of spectral variables, but also improved the discriminating rate.