Multiple adaptive mechanisms for data-driven soft sensors
Authors: Bakirov, R., Gabrys, B. and Fay, D.
Journal: Computers and Chemical Engineering
Volume: 96
Pages: 42-54
ISSN: 0098-1354
DOI: 10.1016/j.compchemeng.2016.08.017
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.
https://eprints.bournemouth.ac.uk/24679/
Source: Scopus
Multiple adaptive mechanisms for data-driven soft sensors
Authors: Bakirov, R., Gabrys, B. and Fay, D.
Journal: COMPUTERS & CHEMICAL ENGINEERING
Volume: 96
Pages: 42-54
eISSN: 1873-4375
ISSN: 0098-1354
DOI: 10.1016/j.compchemeng.2016.08.017
https://eprints.bournemouth.ac.uk/24679/
Source: Web of Science (Lite)
Multiple Adaptive Mechanisms for Data-driven Soft Sensors
Authors: Bakirov, R., Gabrys, B. and Fay, D.
Journal: Computers and Chemical Engineering
Publisher: Elsevier
ISSN: 0098-1354
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.
https://eprints.bournemouth.ac.uk/24679/
Source: Manual
Multiple adaptive mechanisms for data-driven soft sensors.
Authors: Bakirov, R., Gabrys, B. and Fay, D.
Journal: Comput. Chem. Eng.
Volume: 96
Pages: 42-54
DOI: 10.1016/j.compchemeng.2016.08.017
https://eprints.bournemouth.ac.uk/24679/
Source: DBLP
Multiple Adaptive Mechanisms for Data-driven Soft Sensors.
Authors: Bakirov, R., Gabrys, B. and Fay, D.
Journal: Computers and Chemical Engineering
Volume: 96
Issue: January
Pages: 42-54
ISSN: 0098-1354
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.
https://eprints.bournemouth.ac.uk/24679/
Source: BURO EPrints