殷贤华,王宁,陈晶溪.基于太赫兹时域光谱系统的橡胶分类识别[J].国外电子测量技术,2016,35(6):19-23
基于太赫兹时域光谱系统的橡胶分类识别
Rubber classification and recognition based on THz time domain spectroscopy system
投稿时间:2016-03-05  修订日期:2016-03-17
DOI:
中文关键词:  橡胶  KPCA  KCCA  支持向量机  PLS-DA
英文关键词:rubber  KPCA  KCCA  support vector machine  PLS-DA
基金项目:广西自然科学基金项目(2015GXNSFBA139252);广西自动检测技术与仪器重点实验室基金项目(YQ15104)
作者单位
殷贤华 桂林电子科技大学电子工程与自动化学院 桂林 541004 
王宁 桂林电子科技大学电子工程与自动化学院 桂林 541004 
陈晶溪 桂林电子科技大学电子工程与自动化学院 桂林 541004 
AuthorInstitution
Yin Xianhua School of Electrical Engineering and Automation, Guilin University of Electronic Technology, Guilin 541004, China 
Wang Ning (School of Electrical Engineering and Automation, Guilin University of Electronic Technology, Guilin 541004, China 
Chen Jingxi School of Electrical Engineering and Automation, Guilin University of Electronic Technology, Guilin 541004, China 
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中文摘要:
      基于太赫兹时域光谱(THz TDS)系统对4种橡胶样品进行检测,分别采用核主成分分析(KPCA)和核典型相关分析(KCCA)方法对橡胶太赫兹光谱进行特征提取,引入PCA和CCA作为对比,再结合支持向量机(SVM)建立分类模型,对橡胶进行分类识别,最后以偏最小二乘判别法(PLS DA)的识别结果作为参考。结果表明,SVM结合特征提取方法可以对橡胶的光谱进行分类识别,KPCA SVM对吸收谱的分类效果最佳,而PLS DA对折射谱的分类效果要优于SVM,同时,KPCA对光谱的特征提取效果要优于标准的KCCA方法。实验为橡胶的识别分析提供了新的方法。
英文摘要:
      Based on the terahertz time domain spectroscopy system, 4 kinds of rubber samples were detected. Comparing with PCA and CCA, Kernel principal component analysis (KPCA) and kernel canonical correlation analysis (KCCA) were carried out on the feature extraction of rubber terahertz spectrum. The classification model was established by support vector machine (SVM) to classify the rubber samples. Finally, the recognition results of partial least squares (PLS DA) are used as the reference. The experimental results show that SVM can be used to classify the spectrum of rubber combined with the feature extraction methods. The classification effect of KPCA SVM on the absorption spectrum is the best, and PLS DA is better than SVM on refraction spectrum classification. Meanwhile, KPCA is better than the standard KCCA method for the feature extraction of the spectrum. The experiment provides a new method for the identification and analysis of rubber.
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