Output details
11 - Computer Science and Informatics
Lancaster University
Article title
Adaptive inferential sensors based on evolving fuzzy models
Type
D - Journal article
Title of journal
IEEE Transactions on Systems, man and Cybernetics - Part B: Cybernetics
Article number
-
Volume number
40
Issue number
2
First page of article
529
ISSN of journal
1083-4419
Year of publication
2010
Number of additional authors
1
Additional information
<12> This paper, published in well-respected IEEE Transactions (IF=3.6), proposes self-calibrating “evolving sensors” (called eSensors) with a focus on reducing maintenance costs in online data classification systems, while maintaining high precision and interpretability/ transparency. It introduces automatic methods for online selection of the most relevant input variables as well as for detection of shift and drift in the data stream using ageing. Results demonstrate that eSensors can be automatically derived from data streams in real time. The approach has since been applied in industry - e.g. by Dow Chemicals and in a CEPSA oil refinery in Spain.
Interdisciplinary
-
Cross-referral requested
-
Research group
None
Citation count
15
Proposed double-weighted
No
Double-weighted statement
-
Reserve for a double-weighted output
No
Non-English
No
English abstract
-
