| 基于核PCA与SVM相结合的电子鼻模式识别算法研究 |
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| 引用本文： | 金翠云,崔瑶,王颖.基于核PCA与SVM相结合的电子鼻模式识别算法研究[J].北京化工大学学报(自然科学版),2012,39(2):106-109. |
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| 作者姓名： | 金翠云 崔瑶 王颖 |
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| 作者单位： | 北京化工大学信息科学与技术学院,北京,100029;北京化工大学信息科学与技术学院,北京,100029;北京化工大学信息科学与技术学院,北京,100029 |
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| 摘 要： | 将核主元分析(PCA)与支持向量机(SVM)相结合并将其应用到电子鼻模式识别单元中，实现了数据降维和改善分类器性能。实验结果表明与单纯的应用支持向量机方法进行分类相比，此方法具有更高的识别率。
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| 关 键 词： | 电子鼻 核主元分析 支持向量机 |
| 收稿时间： | 2011-08-29 |
A pattern recognition method for electric nose based on kernel-principal component analysis (PCA) and support vector machine (SVM) |
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| JIN CuiYun
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CUI Yao
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WANG Ying.A pattern recognition method for electric nose based on kernel-principal component analysis (PCA) and support vector machine (SVM)[J].Journal of Beijing University of Chemical Technology,2012,39(2):106-109. |
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| Authors: | JIN CuiYun CUI Yao WANG Ying |
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| Institution: | College of Information Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China |
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| Abstract: | This paper presents a pattern recognition method combining kernel-principal component analysis(PCA) and support vector machine(SVM) and its application to electronic nose technology.It can give data reduction and improve the performance of classification by combining the two methods used in complex electronic nose test environments.The experimental results show that the method has higher recognition compared with the simple application of SVM. |
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| Keywords: | electronic nose kernel-principal component analysis(PCA) support vector machine(SVM) |
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