文献类型：期刊文献

中文题名：基于混合SVR-PLS方法的丙烯腈收率软测量建模

英文题名：Soft sensor modeling of acrylonitrile yield based on hybrid SVR-PLS approach

作者：王华忠[1];俞金寿[1]

机构：[1]华东理工大学自动化研究所,上海200237

年份：2005

卷号：20

期号：5

起止页码：549

中文期刊名：控制与决策

外文期刊名：Control and Decision

收录：CSTPCD;;EI(收录号：2005289208084);Scopus;北大核心:【北大核心2004】；CSCD:【CSCD2011_2012】；

语种：中文

中文关键词：支持向量回归机;偏最小二乘法;丙烯腈;软测量

外文关键词：Backpropagation;Feature extraction;Global optimization;Least squares approximations;Neural networks;Nonlinear control systems;Polyacrylonitriles

摘要：为了更有效地处理过程非线性、多输入和数据共线性等复杂特性,提高模型的推广能力和精度,提出了混合支持向量回归机-偏最小二乘法(SVR-PLS)方法.该方法兼具SVR和PLS的优点,用PLS进行特征提取,用SVR建立PLS的内部模型.对工业丙烯腈生产过程丙烯腈收率软测量建模的应用表明,采用该方法建立的软测量模型,在模型精度、推广能力等方面明显优于一些传统软测量建模方法,满足工业应用要求.
A hybrid SVR-PLS method is proposed to deal with complicated process with nonlinearity and a large number of correlated inputs. The SVR-PLS method, which has merits of both SVRs and PLS, is an integration of support vector regression machine and partial least squares. The PLS outer projection is used as a dimension reduction tool to remove collinearity and the SVRs are trained to capture the nonlinearity in the projected latent space. Soft sensor modeling of acrylonitrile yield is established using SVR-PLS method. The generalization ability and accuracy of the soft sensor using the method proposed is superior to traditional methods.