Neural network approach to solving fuzzy nonlinear equations using Z-numbers
- Submitting institution
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University of Portsmouth
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 15521401
- Type
- D - Journal article
- DOI
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10.1109/TFUZZ.2019.2940919
- Title of journal
- IEEE Transactions on Fuzzy Systems
- Article number
- -
- First page
- 1230
- Volume
- 28
- Issue
- 7
- ISSN
- 1063-6706
- Open access status
- Compliant
- Month of publication
- September
- Year of publication
- 2019
- 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
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2
- Research group(s)
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B - Computational Intelligence
- Citation count
- 2
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- A novel application of deep fuzzy models pioneered by the authors. The fuzzy approach with Z-numbers allows for effective modelling of nonlinear systems under uncertainty to improve systems design. Practical relevance to problems in aerospace and solar energy generation has been demonstrated. However, the method has also been used for pipeline defects detection [Razvarz et al., Studies in Systems, Decision and Control, vol 321, 2021] showing a wider impact.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -