Title :
A modified PLS regression model for quality prediction
Author :
Yingwei Zhang ; Lingjun Zhang
Author_Institution :
State Lab. of Synthesis Autom. of Process Ind., Northeastern Univ., Shenyang, China
Abstract :
In this paper, a modified partial least-squares (PLS) regression modeling method is proposed. The proposed method can build a modified regression model to extract the useful information in residual subspace, which is helpful to predict the output variables. With this method, more accurate quality variables are predicted. In simulation experiment, penicillin fermentation process is used to test the proposed modified PLS method and the conventional PLS method is also applied in the process. It is shown that the proposed method is more effective than the conventional PLS method.
Keywords :
least squares approximations; predictive control; process control; product quality; regression analysis; information extraction; modified PLS regression model; modified partial least-squares regression modeling method; penicillin fermentation process; quality prediction; quality variables; residual subspace; Accuracy; Correlation; Mathematical model; Monitoring; Predictive models; Principal component analysis; Training; Modified PLS regression model; Prediction; Quality variables; Residual subspace;
Conference_Titel :
Intelligent Control and Automation (WCICA), 2014 11th World Congress on
DOI :
10.1109/WCICA.2014.7052921
