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

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