Interval type-2 A-intuitionistic fuzzy logic for regression problems
- Submitting institution
-
University of Nottingham, The
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 1321912
- Type
- D - Journal article
- DOI
-
10.1109/TFUZZ.2017.2775599
- Title of journal
- IEEE Transactions on Fuzzy Systems
- Article number
- -
- First page
- 2396
- Volume
- 26
- Issue
- 4
- ISSN
- 1063-6706
- Open access status
- Compliant
- Month of publication
- November
- Year of publication
- 2017
- 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)
-
-
- Citation count
- 13
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- A novel hesitation-enabled rule-based interval type-2 Atanassov-intuitionistic fuzzy logic system (IT2AIFLS) with gradient descent parameter optimisation is introduced for regression problems. IT2AIFLS captures human reasoning explicitly by assigning a membership degree and a non-membership degree with hesitation to every element. The hesitation is a key component that is lacking in traditional fuzzy logic systems. Results of an in-depth analysis show that new IT2AIFLS provide better prediction accuracy compared to type-1 variants, traditional fuzzy logic systems, and many other approaches previously introduced in the literature.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
