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dc.contributor.authorKozel, Tomášcs
dc.contributor.authorStarý, Milošcs
dc.date.accessioned2021-07-23T14:55:20Z
dc.date.available2021-07-23T14:55:20Z
dc.date.issued2019-12-15cs
dc.identifier.citationJournal of Hydrology and Hydromechanics. 2019, vol. 64, issue 4, p. 314-321.en
dc.identifier.issn0042-790Xcs
dc.identifier.other161073cs
dc.identifier.urihttp://hdl.handle.net/11012/200894
dc.description.abstractThe design and evaluation of algorithms for adaptive stochastic control of reservoir function of the water reservoir using artificial intelligence methods (learning fuzzy model and neural networks) are described in this article. This procedure was tested on an artificial reservoir. Reservoir parameters have been designed to cause critical disturbances during the control process, and therefore the influences of control algorithms can be demonstrated in the course of controlled outflow of water from the reservoir. The results of the stochastic adaptive models were compared. Further, stochastic model results were compared with a resultant course of management obtained using the method of classical optimisation (differential evolution), which used stochastic forecast data from real series (100% forecast). Finally, the results of the dispatcher graph and adaptive stochastic control were compared. Achieved results of adaptive stochastic management provide inspiration for continuing research in the field.en
dc.formattextcs
dc.format.extent314-321cs
dc.format.mimetypeapplication/pdfcs
dc.language.isoencs
dc.publisherJournal of Hydrology and Hydromechanicscs
dc.relation.ispartofJournal of Hydrology and Hydromechanicscs
dc.relation.urihttp://www.uh.sav.sk/Portals/16/vc_articles/2019_67_4_Kozel_314.pdfcs
dc.rightsCreative Commons Attribution-NonCommercial-NoDerivatives 3.0 Unportedcs
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/cs
dc.subjectStochasticen
dc.subjectArtificial intelligenceen
dc.subjectStorage functionen
dc.subjectOptimisationen
dc.titleAdaptive stochastic management of the storage function for a large open reservoir using an artificial intelligence methoden
thesis.grantorVysoké učení technické v Brně. Fakulta stavební. Ústav vodního hospodářství krajinycs
sync.item.dbidVAV-161073en
sync.item.dbtypeVAVen
sync.item.insts2021.07.23 16:55:20en
sync.item.modts2021.07.23 16:14:14en
dc.coverage.issue4cs
dc.coverage.volume64cs
dc.identifier.doi10.2478/johh-2019-0021cs
dc.rights.accessopenAccesscs
dc.rights.sherpahttp://www.sherpa.ac.uk/romeo/issn/0042-790X/cs
dc.type.driverarticleen
dc.type.statusPeer-revieweden
dc.type.versionpublishedVersionen


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Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 Unported
Except where otherwise noted, this item's license is described as Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 Unported