GA-based learning for rule identification in fuzzy neural networks
- Submitting institution
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Teesside University
- Unit of assessment
- 12 - Engineering
- Output identifier
- 7086387
- Type
- D - Journal article
- DOI
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10.1016/j.asoc.2015.06.046
- Title of journal
- Applied Soft Computing
- Article number
- -
- First page
- 605
- Volume
- 35
- Issue
- -
- ISSN
- 1568-4946
- Open access status
- Out of scope for open access requirements
- Month of publication
- -
- Year of publication
- 2015
- 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
- This article employed an effective learning process in designing a fuzzy neural network, especially when expert knowledge is not available. The article attracted significant attention within a short period and the proposed research framework has been further improved and adopted (e.g., Li et al, 2016, 10.1007/s00521-016-2707-8; Han et al, 2016, 10.1016/j.neucom.2016.07.003). The research was supported by the Saudi Traffic Police Authority to address their traffic management challenges.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -