Multiple Adaptive Mechanisms for Data-driven Soft Sensors.

Bakirov, R., Gabrys, B. and Fay, D., 2016. Multiple Adaptive Mechanisms for Data-driven Soft Sensors. Computers and Chemical Engineering. (In Press)

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Abstract

Recent data-driven soft sensors often use multiple adaptive mechanisms to cope with non-stationary environments. These mechanisms are usually deployed in a prescribed order which does not change. In this work we use real world data from the process industry to compare deploying adaptive mechanisms in a fixed manner to deploying them in a flexible way, which results in varying adaptation sequences. We demonstrate that flexible deployment of available adaptive methods coupled with techniques such as cross-validatory selection and retrospective model correction, can benefit the predictive accuracy over time. As a vehicle for this study, we use a soft-sensor for batch processes based on an adaptive ensemble method which employs several adaptive mechanisms to react to the changes in data.

Item Type:Article
ISSN:0098-1354
Uncontrolled Keywords:Soft sensors; Adaptive mechanisms; Streaming data; Ensemble method
Subjects:UNSPECIFIED
Group:Faculty of Science & Technology
ID Code:24679
Deposited By: Unnamed user with email symplectic@symplectic
Deposited On:01 Sep 2016 15:36
Last Modified:01 Sep 2016 15:36

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