Learning dynamical models using motifs
Loading...
Files
Published Version
Date
2016-09
Authors
Provan, Gregory
Journal Title
Journal ISSN
Volume Title
Publisher
Sun SITE Central Europe / RWTH Aachen University
Published Version
Abstract
Automatically creating dynamical system models, M, from data is an
active research area for a range of real-world applications, such as systems biology
and engineering. However, the overall inference complexity increases exponentially
in terms of the number of variables in M. We solve this exponential
growth by using canonical representations of system motifs (building blocks) to
constrain the model search during automated model generation. The motifs provide
a good prior set of building blocks from which we can generate system-level
models, and the canonical representation provides a theoretically sound framework
for modifying the equations to improve the initial models. We present an
automated method for learning dynamical models from motifs, such that the models
optimize a domain-specific performance metric.We demonstrate our approach
on hydraulic systems models.
Description
Keywords
Model , Motif , Canonical , Dynamical , Hydraulic
Citation
Provan, Gregory (2016) 'Learning dynamical models using motifs', in Greene, D., MacNamee, B. and Ross, R. (eds.) Proceedings of the 24th Irish Conference on Artificial Intelligence and Cognitive Science, Dublin, Ireland, 20-21 September. CEUR Workshop Proceedings, 1751, pp. 161-172