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Multiple Adaptive Mechanisms for Data-driven Soft Sensors.

Bakirov, R., Gabrys, B. and Fay, D., 2017. Multiple Adaptive Mechanisms for Data-driven Soft Sensors. Computers and Chemical Engineering, 96 (January), 42-54.

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CACE_revisions.pdf - Accepted Version
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DOI: 10.1016/j.compchemeng.2016.08.017


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
Uncontrolled Keywords:Soft sensors; Adaptive mechanisms; Streaming data; Ensemble method
Group:Faculty of Science & Technology
ID Code:24679
Deposited By: Symplectic RT2
Deposited On:01 Sep 2016 15:36
Last Modified:14 Mar 2022 13:58


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