A Novel Variable Selection Approach for Redundant Information Elimination Purpose of Process Control

Abstract

In this paper, an efficient variable and subset selection approach related to modified partial least squares (PLS) regression are proposed and applied to numerical and industry application to show the superior of the results. The main purpose of the proposed variable selection approach is to offer further useful information to identification, control as well as diagnosis. The significant advantage of the modified PLS-based variable selection method is to keep the superior fitting and prediction performance as well as the significantly reduced number of selected variables. To this aim, the canonical and modified PLS regression methods have been introduced. Then an effective and precise variable selection method which can select the most significant elements among huge redundant information is proposed. To this end, the effectiveness of the proposed variable selection method is demonstrated by the simulation results of a numerical instance and a process industry case study.