GA-based learning for rule identification in fuzzy neural networks
- Submitting institution
-
University of the West of Scotland
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 12461137
- Type
- D - Journal article
- DOI
-
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
- October
- 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
- No
- Number of additional authors
-
3
- Research group(s)
-
-
- Citation count
- 15
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- Novel GA-based pruning approach for tackling fuzzy associative matrix (FAM) construction challenge is proposed and demonstrated in an intelligent system application to identify coordinated road traffic control actions for an incident from a global view. The research was funded by Saudi Government to analyses the traffic problem and help the traffic centre to find global solution fast during Hajj period in Riyadh. Co-author has been employed by Riyadh traffic police to lead the implementation of the proposed intelligent system to control the road traffic congestion. This comprehensive paper was followed by our conference and journal articles (MSC2009; DSS-journal-2013).
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -