计算机工程与应用 ›› 2011, Vol. 47 ›› Issue (21): 235-238.

• 工程与应用 • 上一篇    下一篇

局部KPLS特征提取的LSSVM软测量建模方法

李雅芹,杨慧中   

  1. 江南大学 通信与控制工程学院,江苏 无锡 214122
  • 收稿日期:1900-01-01 修回日期:1900-01-01 出版日期:2011-07-21 发布日期:2011-07-21

Soft sensor modeling based on local KPLS feature extraction and on-line LSSVM

LI Yaqin,YANG Huizhong   

  1. School of Communication & Control Engineering,Jiangnan University,Wuxi,Jiangsu 214122,China
  • Received:1900-01-01 Revised:1900-01-01 Online:2011-07-21 Published:2011-07-21

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。

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

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.

Key words: Kernel Partial Least Squares(KPLS), on-line Least Squares Support Vector Machines(LSSVM), local learning, feature extraction