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.

http://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

http://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.

http://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

http://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.

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

Source: BURO EPrints