中文摘要：

利用自编码网络（autoencodernetwork，AN）流形学习和稀疏表示（sparserepresentation，SR）方法对汽车变速箱油进行近红外光谱品种识别研究。以壳牌、美孚、嘉实多、上海大众和上海通用五种变速箱油为对象，利用AN方法对600-1800nm近红外光谱数据进行非线性降维，获取10个特征变量。每种变速箱油选取30个样本（共150个样本）作为训练样本，每种30个样本（共150个样本）作为测试样本。所有训练样本的特征变量组成了稀疏表示方法的整体训练样本矩阵，将变速箱油品种分类识别问题转化为一个求解待识别测试样本对于整体训练样本矩阵的稀疏表示问题，通过求解L-1范数意义下的最优化问题来实现。经过主成分分析（principalcomponentanalysis，PCA）和AN降维后，分别利用线性判断分析法（1ineardiscrimi—nantanalysis，LDA）、偏最小二乘支持向量机法（1eastsquares—supportvectormachine，LS-SVM）和本文提出的稀疏表示分类算法进行分类比较。结果表明，结合自编码网络和稀疏表示方法对五种汽车变速箱油品种的平均识别准确率达97．33％，为汽车变速箱油品种近红外光谱快速准确识别提供了有效的新途径。

英文摘要：

An identification method based on sparse representation （SR） combined with autoencoder network （AN） manifold learning was proposed for discriminating the varieties of transmission fluid by using near infrared （NIR） spectroscopy technolo- gy. NIR transmittance spectra from 600 to 1 800 nm were collected from 300 transmission fluid samples of five varieties （each variety consists of 60 samples）. For each variety, 30 samples were randomly selected as training set （totally 150 samples）, and the rest 30 ones as testing set （totally 150 samples）. Autoencoder network manifold learning was applied to obtain the character- istic information in the 600- 1 800 nm spectra and the number of characteristics was reduced to 10. Principal component analysis （PCA） was applied to extract several relevant variables to represent the useful information of spectral variables. All of the train- ing samples made up a data dictionary of the sparse representation （SR）. Then the transmission fluid variety identification prob- lem was reduced to the problem as how to represent the testing samples from the data dictionary （training samples data）. The i- dentification result thus could be achieved by solving the L-1 norm-based optimization problem. We compared the effectiveness of the proposed method with that of linear discriminant analysis （LDA）, least squares support vector machine （LS-SVM） and sparse representation （SR） using the relevant variables selected by principal component analysis （PCA） and AN. Experimental results demonstrated that the overall identification accuracy of the proposed method for the five transmission fluid varieties was 97. 33% by AN-SR, which was significantly higher than that of LDA or LS-SVM. Therefore, the proposed method can provide a new effective method for identification of transmission fluid variety.