QML-AiNet : an immune network approach to learning qualitative differential equation models
- Submitting institution
-
University of Aberdeen
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 67439570
- Type
- D - Journal article
- DOI
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10.1016/j.asoc.2014.11.008
- Title of journal
- Applied Soft Computing
- Article number
- -
- First page
- 148
- Volume
- 27
- Issue
- -
- ISSN
- 1568-4946
- Open access status
- Out of scope for open access requirements
- Month of publication
- November
- Year of publication
- 2014
- 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
-
1
- Research group(s)
-
-
- Citation count
- 5
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- The interdisciplinary research presented in the paper was funded by the BBSRC SABR initiative (CRISP project, £5M).
This research describes a novel method for learning the structure of qualitative differential equation models. It also describes a general improvement to the immune network algorithm used.The paper is significant because it is the first successful application of immune inspired network algorithms to qualitative model learning.
The approach used to search the discrete qualitative model space can be generalised to solve other discrete optimisation problems.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
