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Latent Variable Methods

Latent variable (LV) techniques such as principal components analysis (PCA) and partial least square (PLS) area at the core of many of the developments in data analytics techniques within MACC. These methods compress the data into a low dimensional space that captures the bulk of the variation in the data. Applications include process monitoring and fault detection, model predictive control, image analysis, and product design. Current work includes use of LV methods for monitoring the performance of supply chain systems, and detecting abnormal conditions such as transportation delays.

Dr. Chris L. E. Swartz
Professor and Director, MACC
Jing Wang
Ph.D. Candidate
Latent Variable Model Predictive Control for Trajectory Tracking in Batch Processes: Alternative Modeling Approaches
J Proc Contr, 21 (9) 1345-1358 (2011)  -  [ Publisher Version ]
Latent variable MPC for trajectory tracking in batch processes
Flores-Cerrillo, J., MacGregor, J. F.
Journal of Process Control, 15 651-663 (2005)  -  [ Publisher Version ]