局部KPLS特征提取的LSSVM软测量建模方法

摘要

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.%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.