Improved uncertainty capture for nonsingleton fuzzy systems
- Submitting institution
-
Nottingham Trent University
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 18 - 700300
- Type
- D - Journal article
- DOI
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10.1109/TFUZZ.2016.2540065
- Title of journal
- IEEE Transactions on Fuzzy Systems
- Article number
- -
- First page
- 1513
- Volume
- 24
- Issue
- 6
- ISSN
- 1063-6706
- Open access status
- Out of scope for open access requirements
- Month of publication
- March
- Year of publication
- 2016
- URL
-
-
- Supplementary information
-
-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
-
3
- Research group(s)
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A - Computing and Informatics Research Centre
- Citation count
- 15
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- By altering the mathematics of the classical fuzzy relations composition, this paper introduces a novel inference method in non-singleton fuzzy rule-based systems. Since the method has shown a significant outperformance over the classical fuzzy systems for noisy and chaotic time series prediction, the paper suggests that the method can similarly improve performance in a wide range of uncertain decision support systems, e.g., in healthcare, autonomous vehicles and finance. Since published, this paper led a series of further improvements (e.g., 10.1109/FUZZ-IEEE.2017.8015575, 10.1109/FUZZ-IEEE.2016.7737703 working with Nottingham University) and industrial applications in Quadcopter and UAV control (10.1109/TMECH.2018.2810947, 10.1109/FUZZ-IEEE.2016.7737800 at Engineering-NTU Corp Laboratory, Singapore).
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
