Adaptive continuous hierarchical model-based decision making

0361358 - ÚTIA 2012 RIV PT eng C - Conference Paper (international conference)
Dedecius, Kamil - Ettler, P.
Adaptive continuous hierarchical model-based decision making.
Proceedings of the 8th International Conference on Informatics in Control, Automation and Robotics. Portugalsko: SciTePress – Science and Technology Publications, 2011, s. 284-289. ISBN 978-989-8425-74-4.
[8th International Conference on Informatics in Control, Automation and Robotics (ICINCO). Noordwijkerhout (NL), 27.07.2011-31.07.2011]
R&D Projects: GA MŠMT(CZ) 7D09008; GA MŠMT 1M0572
Institutional research plan: CEZ:AV0Z10750506
Keywords : Bayesian modelling * Hierarchical model * Parameter estimation
Subject RIV: BB - Applied Statistics, Operational Research
http://library.utia.cas.cz/separaty/2011/AS/dedecius-adaptive continuous hierarchical model-based decision making.pdf

Industrial model-based control often relies on parametric models. However, for certain operational conditions either the precise underlying physical model is not available or the lack of relevant or reliable data prevents its use. A popular approach is to employ the black box or grey box models, releasing the theoretical rigor. This leads to several candidate models being at disposal, from which the (often subjectively) prominent one is selected. However, in the presence of model uncertainty, we propose to benefit from a subset of credible models. The idea behind the multimodelling approach is closely related to hierarchical modelling methodology. By using several modelling levels, it is possible to achieve relatively high quality and robust solution, providing a way around typical constraints in industrial applications.
Permanent Link: http://hdl.handle.net/11104/0198685