| 基于KPCA-LSSVM的软测量建模方法 |
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| 引用本文： | 王强,田学民.基于KPCA-LSSVM的软测量建模方法[J].化工学报,2011,62(10):2813-2817. |
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| 作者姓名： | 王强 田学民 |
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| 作者单位： | 中国石油大学(华东)信息与控制工程学院,山东东营,257061 |
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| 基金项目： | 国家高技术研究发展计划项目(2007AA04Z193)~~ |
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| 摘 要： | 提出了一种将核主成分分析(KPCA)和最小二乘支持向量机(LSSVM)相结合的软测量建模方法.核主成分分析能够对样本数据进行特征提取,消除数据的相关性.本文利用KPCA提取主元,降低样本的维数；然后利用最小二乘支持向量机进行建模,不仅降低了模型的复杂性,而且提高了模型的泛化能力.用该方法建立柴油凝点的软测量模型,和其他...
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| 关 键 词： | 软测量 核主成分分析(KPCA) 最小二乘支持向量机(LSSVM) 特征提取 |
Soft sensing based on KPCA and LSSVM |
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| WANG Qiang,TIAN Xuemin.Soft sensing based on KPCA and LSSVM[J].Journal of Chemical Industry and Engineering(China),2011,62(10):2813-2817. |
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| Authors: | WANG Qiang TIAN Xuemin |
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| Affiliation: | WANG Qiang,TIAN Xuemin(College of Information and Control Engineering,China University of Petroleum,Dongying 257061,Shandong,China) |
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| Abstract: | A kind of soft sensing is proposed by combining kernel principal component analysis(KPCA)with least square support vector machine(LSSVM).The KPCA has excellent performance of feature extraction and can eliminate the correlation of the input.The KPCA is used to choose the principal component and reduce dimensions of sample.Then LSSVM is applied to proceed regression modelling,which can not only reduce the complexity of modeling but also improve the generalization ability.The proposed method is used to build ... |
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| Keywords: | soft sensing kernel principal component analysis least square support vector machine feature extraction |
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