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Model Mixing for Long-Term Extrapolation
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SYSNO ASEP 0086418 Document Type A - Abstract R&D Document Type The record was not marked in the RIV R&D Document Type Není vybrán druh dokumentu Title Model Mixing for Long-Term Extrapolation Title Míchání modelů pro dlouhodobou extrapolaci Author(s) Ettler, P. (CZ)
Kárný, Miroslav (UTIA-B) RID, ORCID
Nedoma, Petr (UTIA-B)Source Title Proceedings of the 6th EUROSIM Congress on Modelling and Simulation - Vol.1: Book of Abstracts. - Vienna : ARGESIM-ARGE Simulation News, 2007
S. 275-275Number of pages 1 s. Publication form WWW - WWW Action EUROSIM Congress on Modelling and Simulation /6./ Event date 09.09.2007-13.09.2007 VEvent location Ljubljana Country SI - Slovenia Event type WRD Language eng - English Country AT - Austria Keywords Simulation ; Modelling ; Estimation ; Multiple models Subject RIV BB - Applied Statistics, Operational Research R&D Projects 1ET100750401 GA AV ČR - Academy of Sciences of the Czech Republic (AV ČR) 1M0572 GA MŠMT - Ministry of Education, Youth and Sports (MEYS) CEZ AV0Z10750506 - UTIA-B (2005-2011) Annotation Reliable extrapolation - simulation or prediction - of system output is an invaluable departure point for the control system design. For application of model-based techniques, the knowledge of the model structure is essential. It can be based purely on the physical point of view or estimated from process data while the system is considered as a "black box". Mixing of both methods results in "grey box" modelling. Often, modelled systems are governed by several known physical laws and each of these laws implies a model, which should match the data. Nevertheless inevitable uncertainties often make simulated outputs of respective models unreliable. The problem is especially pronounced for systems with a significant time delay. This motivates search for methods, which utilize all available models at once and mix their outputs with the aim to get better results. In the paper, four variants of mixing are considered, discussed and their performance compared on industrial data. Seeming alternative -- a simple complex model is discussed as well. Data for experiments came from a cold rolling mill. Workplace Institute of Information Theory and Automation Contact Markéta Votavová, votavova@utia.cas.cz, Tel.: 266 052 201. Year of Publishing 2008
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