QML-AiNet : An immune network approach to learning qualitative differential equation models
- Submitting institution
-
Heriot-Watt University
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 29184652
- 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
- February
- 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
-
1
- Research group(s)
-
-
- Citation count
- 5
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This is one of a series of papers related to learning qualitative differential equation models in the BBSRC funded CRISP project (SABR initiative: £5M). Built upon previous qualitative model learning work on the same project, the developed approach significantly improved the scalability and performance of learning through an immune-inspired framework. The approach developed directly fed into the CRISP project, which uses qualitive model learning to understand a key biological problem and guide biological experiments. The framework developed in this research led to two follow-up papers: Wu et al, Soft Computing 19(6) and Wu et al, Cognitive Computation 7(6).
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
