Learning dynamical models using motifs

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dc.contributor.authorProvan, Gregory
dc.contributor.editorGreene, Derek
dc.contributor.editorMacNamee, Brian
dc.contributor.editorRoss, Robert
dc.contributor.funderScience Foundation Irelanden
dc.date.accessioned2017-08-15T08:55:58Z
dc.date.available2017-08-15T08:55:58Z
dc.date.issued2016-09
dc.date.updated2017-08-11T11:48:08Z
dc.description.abstractAutomatically 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.en
dc.description.sponsorshipScience Foundation Ireland (SFI Grants 12/RC/2289 and 13/RC/2094)en
dc.description.statusPeer revieweden
dc.description.urihttp://aics2016.ucd.ie/en
dc.description.versionPublished Versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.citationProvan, 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. 161-172en
dc.identifier.endpage172
dc.identifier.issn16130073
dc.identifier.journaltitleCEUR Workshop Proceedingsen
dc.identifier.startpage161
dc.identifier.urihttps://hdl.handle.net/10468/4460
dc.identifier.volume1751en
dc.language.isoenen
dc.publisherSun SITE Central Europe / RWTH Aachen Universityen
dc.relation.ispartof24th Irish Conference on Artificial Intelligence and Cognitive Science 2016
dc.relation.urihttp://ceur-ws.org/Vol-1751/
dc.rights© 2016, Gregory Provan.en
dc.rights.urihttp://ceur-ws.org/en
dc.subjectModelen
dc.subjectMotifen
dc.subjectCanonicalen
dc.subjectDynamicalen
dc.subjectHydraulicen
dc.titleLearning dynamical models using motifsen
dc.typeConference itemen
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