Bakirov, R., Gabrys, B. and Fay, D., 2017. Multiple Adaptive Mechanisms for Data-driven Soft Sensors. Computers and Chemical Engineering, 96 (January), 42-54.
Full text available as:
|
PDF
CACE_revisions.pdf - Accepted Version Available under License Creative Commons Attribution Non-commercial No Derivatives. 1MB | |
Copyright to original material in this document is with the original owner(s). Access to this content through BURO is granted on condition that you use it only for research, scholarly or other non-commercial purposes. If you wish to use it for any other purposes, you must contact BU via BURO@bournemouth.ac.uk. Any third party copyright material in this document remains the property of its respective owner(s). BU grants no licence for further use of that third party material. |
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.
Item Type: | Article |
---|---|
ISSN: | 0098-1354 |
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 |
Downloads
Downloads per month over past year
Repository Staff Only - |