基于支持向量机的非线性荧光光谱的识别  

Recognition of Nonlinear Fluorescence Spectrum of Support Vector Machine Networks

作　　者：李素梅[1] 韩应哲[1] 张延炘[1] 常胜江[1] 申金媛[1] 

机构地区：[1]南开大学信息技术科学学院教育部光电信息技术重点实验室,天津300071

出　　处：《光学学报》2006年第1期147-151,共5页Acta Optica Sinica

基　　金：国家自然科学基金(60277022;60477009);天津市自然科学基金重点项目(023800811);天津市科技攻关培育项目(043100811);博士点基金资助项目(20030055022);南开大学科技创新基金资助项目

摘　　要：提出将支持向量机网络应用于含不同浓度杂质气体的非线性荧光光谱的识别。由于原始光谱数据的光谱通道数目很大,首先用小波变换去噪压缩,然后采用主成分分析方法对光谱信息进行连续两次的特征提取。在保持原光谱数据主要信息基本不变的情况下,将数据维数由3979压缩到514(小波变换)并提取9个主成分。这样,不仅减少了网络的输入维数,而且加快了网络的训练速度。实验结果表明,无论对训练样本还是未学习过的测试样本,其正确识别率均可达到100%。网络的训练和测试速度较快,可以更有效地应用于大气杂质气体的实时监测。That the support vector machine network is applied to recognize the nonlinear fluorescence spectrum of impurities of different concentrations in air is proposed. Because the number of spectrum channel of the original spectrum data is large, it is cleaned up and compressed through wavelet trausform firstly, and then the principal component analysis （PCA） is used to extract the character information twice in series. It not only ensures the character of original nonlinear fluorescence spectrum, but also compresses the data number the nonlinear fluorescence spectrum from 3979 to 514, and extracts 9 principal components, which reduces the number of the input vector and improves the training speed of the network. The simulation results show that the correct recognition rates for both training spectrum samples and unlearned test spectrum samples reach 100%. So, the training and testing speed is fast enough to monitor the atmospherical impurity in air in real time,

关 键 词：光谱学 非线性荧光光谱 支持向量机 小波变换 主成分分析 

分 类 号：O433.4[机械工程—光学工程] TP183[理学—光学]