Generalized Adaptive Fuzzy Rule Interpolation
- Submitting institution
-
University of Northumbria at Newcastle
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 22062448
- Type
- D - Journal article
- DOI
-
10.1109/TFUZZ.2016.2582526
- Title of journal
- IEEE Transactions on Fuzzy Systems
- Article number
- -
- First page
- 839
- Volume
- 25
- Issue
- 4
- ISSN
- 1063-6706
- Open access status
- Compliant
- Month of publication
- July
- 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
-
2
- Research group(s)
-
D - Computer Vision and Natural Computing (CVNC)
- Citation count
- 39
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- Selected as a highlighted paper by the IEEE Computational Intelligence Society due to its significance in enabling self-evolving fuzzy inference systems (CIS Publication Spotlight, IEEE Computational Intelligence Magazine, 13 (1), pp.25-27, 2018) and received a Best Paper Award (16th UK and Ireland Annual Workshop on Computational Intelligence). Underpinned the subsequent award of a Royal Academy of Engineering project “Anomaly Traffic Identification through Artificial Intelligence, Cyber Security and Big Data Analytics Technologies”, (£50k, IAPP1\100077) in collaboration with Mae Fah Luang University (Thailand), Defence School of Communications and Information Systems (MOD, UK), Royal Thai Air Force, T-Net Co. Ltd and MOD (Thailand).
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -