文献类型：期刊文献

中文题名：一种基于高斯过程的非线性PLS建模方法

英文题名：A Nonlinear Partial Least Square Modeling Method Based on Gaussian Process

作者：王华忠[1]

机构：[1]华东理工大学自动化研究所,上海200237

年份：2007

卷号：33

期号：5

起止页码：708

中文期刊名：华东理工大学学报（自然科学版）

外文期刊名：Journal of East China University of Science and Technology

收录：CSTPCD;;Scopus;北大核心:【北大核心2004】；CSCD:【CSCD2011_2012】；

语种：中文

中文关键词：高斯过程(GP);偏最小二乘法(PLS);丙烯腈;软测量

外文关键词：Gaussian process（GP）; partial lease squares（PLS）; acrylonitrile; soft sensing

摘要：提出了一种基于高斯过程(GP)和偏最小二乘法(PLS)的非线性PLS方法(GP-PLS),以更加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。该方法首先采用PLS进行特征提取,再用GP建立PLS的内部模型,因而具有GP与PLS的优点。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,满足工业现场应用要求。
A new nonlinear partial least squares method （GP-PLS） based on Gaussian process is proposed to deal with complicated processes with nonlinearities and a large number of correlated inputs. The GP-PLS method,which has merits of both GP and PLS,is an integration of GP models and partial least squares. The PLS outer projection is used as a dimension reduction tool to remove collinearity and the GP models are trained to capture the nonlinearities in the projected latent space. Soft sensor modeling of acrylonitrile yield using GP-PLS method is established. It is found that the generalization ability and the accuracy of the soft sensor using the method proposed are superior to traditional methods, and the performance of the soft sensor meets the demands of industrial application in the field.