文献类型：期刊文献

英文题名：Nonlinear partial least square modeling method based on Gaussian process

作者：Wang, Hua-Zhong

机构：[1] Research Institute of Automation, East China University of Science and Technology, Shanghai 200237, China

年份：2007

卷号：33

期号：5

起止页码：708

外文期刊名：Huadong Ligong Daxue Xuebao /Journal of East China University of Science and Technology

收录：EI(收录号：20074710937362)

语种：英文

摘要：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.