Multiple adaptive mechanisms for data-driven soft sensors

Authors: Bakirov, R., Gabrys, B. and Fay, D.

http://eprints.bournemouth.ac.uk/24679/

Journal: Computers and Chemical Engineering

Publisher: Elsevier

ISSN: 0098-1354

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.

This source preferred by Bogdan Gabrys, Damien Fay and Rashid Bakirov

This data was imported from Scopus:

Authors: Bakirov, R., Gabrys, B. and Fay, D.

http://eprints.bournemouth.ac.uk/24679/

Journal: Computers and Chemical Engineering

Volume: 96

Pages: 42-54

ISSN: 0098-1354

DOI: 10.1016/j.compchemeng.2016.08.017

© 2016 Elsevier LtdRecent 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.

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