Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
10332828 | Journal of Computational Science | 2014 | 14 Pages |
Abstract
In this paper, a novel qualitative differential equation model learning (QML) framework named QML-Morven is presented. QML-Morven employs both symbolic and evolutionary approaches as its learning strategies to deal with models of different complexity. Based on this framework, a series of experiments were designed and carried out to: (1) investigate factors that influence the learning precision and minimum data requirement for successful learning; (2) address the scalability issue of QML systems.
Related Topics
Physical Sciences and Engineering
Computer Science
Computational Theory and Mathematics
Authors
Wei Pang, George Macleod Coghill,