An Efficient LS-SVM-Based Method for Fuzzy System Construction
- Submitting institution
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The University of East Anglia
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 183394972
- Type
- D - Journal article
- DOI
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10.1109/TFUZZ.2014.2321594
- Title of journal
- IEEE Transactions on Fuzzy Systems
- Article number
- -
- First page
- 627
- Volume
- 23
- Issue
- 3
- ISSN
- 1063-6706
- Open access status
- Out of scope for open access requirements
- Month of publication
- June
- Year of publication
- 2015
- 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
- Yes
- Number of additional authors
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2
- Research group(s)
-
-
- Citation count
- 11
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This research provides new insights to extract sparse fuzzy rules for regression and classification problems by developing a two-phase learning method. The research was funded by EPSRC (EP/L001063/1, EP/G042594/1) and has strengthened the collaboration between 10+ leading UK and Chinese organisations including QUB, Tsinghua University, Chinese Academy of Science and China State Construct Eng Corp. This work has resulted in new collaborations for the IUK grant Remedy (105843), a £5.4M new partnership with Imperial, Southend Council, SMS and Vital Energi. It also led to the award of a KTP project on packaging automation (12228) and an NIHR project inflAIM (202652).
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -