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