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Eliminating the Effect of Multivariate Outliers in PLS-Based Models for Inferring Process Quality
Published in ELSEVIER
Volume: 26
Pages: 755 - 760
Outliers in multivariate data demand special attention in data-driven process modeling. Their extremeness usually gives them an excessively high influence in the calculation, which may result in a less precise model. It is challenging to detect them using existing univariate approaches. A novel robust modeling method is presented; this PLS based modeling procedure not only alleviates the harmful effect of multivariate outliers, but also retains the information necessary for building a robust model from the training data. The performance of the proposed approach is compared with conventional strategies using an actual industrial case study. © 2009 Elsevier B.V. All rights reserved.
About the journal
JournalData powered by TypesetComputer Aided Chemical Engineering
PublisherData powered by TypesetELSEVIER
Open AccessNo