An Extended Takagi-Sugeno-Kang Inference System (TSK+) with Fuzzy Interpolation and Its Rule Base Generation
- Submitting institution
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Teesside University
- Unit of assessment
- 12 - Engineering
- Output identifier
- 7442817
- Type
- D - Journal article
- DOI
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10.1007/s00500-017-2925-8
- Title of journal
- Soft Computing
- Article number
- -
- First page
- 3155
- Volume
- 22
- Issue
- 10
- ISSN
- 1432-7643
- Open access status
- Compliant
- Month of publication
- -
- Year of publication
- 2017
- URL
-
-
- Supplementary information
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-
- 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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3
- Research group(s)
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-
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- The work is the first attempt to perform the TSK fuzzy inference approach over an incomplete knowledge base, which extends the applicability of the TSK fuzzy inference approach. Detailed integral parts and various theoretical extensions of this work have been published in a number of international conferences leading to Best Paper Awards at the UK Workshop on Computational Intelligence International Conference in 2016 and 2018 and IEEE Computational Intelligence Society Outstanding Student Paper Travel Grants in 2017 and 2019.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -