Small Moving Window Calibration Models for Soft Sensing Processes with Limited History

31 Oct 2017 Casey Kneale Steven D. Brown

Five simple soft sensor methodologies with two update conditions were compared on two experimentally-obtained datasets and one simulated dataset. The soft sensors investigated were moving window partial least squares regression (and a recursive variant), moving window random forest regression, the mean moving window of $y$, and a novel random forest partial least squares regression ensemble (RF-PLS), all of which can be used with small sample sizes so that they can be rapidly placed online... (read more)

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