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。
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.
出处
《计算机工程与应用》
CSCD
北大核心
2011年第21期235-238,共4页
Computer Engineering and Applications
基金
国家自然科学基金No.60674092
江苏省高技术研究项目(工业部分)(No.BG2006010)
江南大学创新团队发展计划资助项目~~
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
Kernel Partial Least Squares（KPLS）
on-line Least Squares Support Vector Machines（LSSVM）
local learning
feature extraction
