Mixture Semisupervised Bayesian Principal Component Regression for Soft Sensor Modeling

Mixture Semisupervised Bayesian Principal Component Regression for Soft Sensor Modeling

In this paper, a mixture semisupervised Bayesian principal component regression-based soft sensor modeling method for nonlinear industrial process with multiple operating modes is presented. In many chemistry processes, part of output data samples may be unavailable due to the difficulties in measur...

Full description

Bibliographic Details
Main Authors: Pengbo Zhu, Xin Liu, Yanbo Wang, Xianqiang Yang
Format: Article
Language:English
Published: IEEE 2018-01-01
Series:IEEE Access
Subjects:
Principal component analysis
semisupervised learning
mixture models
expectation-maximization algorithms
Bayesian regularization
Online Access:https://ieeexplore.ieee.org/document/8418691/

Internet

https://ieeexplore.ieee.org/document/8418691/

Similar Items