| 局部KPLS特征提取的LSSVM软测量建模方法 |
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| 引用本文： | 李雅芹,杨慧中.局部KPLS特征提取的LSSVM软测量建模方法[J].计算机工程与应用,2011,47(21):235-238. |
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| 作者姓名： | 李雅芹 杨慧中 |
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| 作者单位： | 江南大学通信与控制工程学院,江苏无锡,214122 |
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| 基金项目： | 国家自然科学基金，江.苏省高技术研究项目(工业部分)，江南大学创新团队发展计划资助项目 |
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| 摘 要： | 针对复杂工业过程的非线性、变量间的强相关性以及工况时变的特点,提出了一种基于局部KPLS特征提取的LSSVM建模方法。该方法通过属性加权的欧式距离指标选取局部训练样本子集,利用KPLS算法对该子集进行特征提取,使用LSSVM算法在线建立局部软测量模型。实验结果表明,该方法可以有效实现特征提取,具有更好的推广能力和预测精度,比基于全局KPLS特征提取的LSSVM模型和未经特征提取的全局LSSVM模型具有更好的泛化能力。
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| 关 键 词： | 核偏最小二乘 在线最小二乘支持向量机(LSSVM) 局部学习 特征提取 |
| 修稿时间： | |
Soft sensor modeling based on local KPLS feature extraction and on-line LSSVM |
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| LI Yaqin,YANG Huizhong.Soft sensor modeling based on local KPLS feature extraction and on-line LSSVM[J].Computer Engineering and Applications,2011,47(21):235-238. |
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| Authors: | LI Yaqin YANG Huizhong |
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| Affiliation: | School of Communication & Control Engineering,Jiangnan University,Wuxi,Jiangsu 214122,China |
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| Abstract: | To deal with complex industrial process variables with strong correlation,non-linearity and time-varying characteristics of operation condition,a new soft sensor modeling method is proposed based on local Kernel Partial Least Squares（KPLS） feature extraction and on-line LSSVM.Some similar samples are found out with the current test sample from the whole sample space,and features of the subspace are extracted,and then a local soft sensor model based on LSSVM is built to estimate the current output.Experimental results show that this method can effectively realize feature extraction,and have a better generalization ability than off-line LSSVM based on global feature extraction with KPLS as well as global LSSVM without feature extraction. |
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| Keywords: | Kernel Partial Least Squares（KPLS） on-line Least Squares Support Vector Machines（LSSVM） local learning feature extraction |
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