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Use of artificial intelligence elements in predictive process management

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dc.title Use of artificial intelligence elements in predictive process management en
dc.contributor.author Blahová, Marta
dc.relation.ispartof International Multidisciplinary Scientific GeoConference Surveying Geology and Mining Ecology Management, SGEM
dc.identifier.issn 1314-2704 Scopus Sources, Sherpa/RoMEO, JCR
dc.date.issued 2022
utb.relation.volume 22
utb.relation.issue 2.1
dc.citation.spage 97
dc.citation.epage 104
dc.event.title 22nd International Multidisciplinary Scientific Geoconference: Informatics, Geoinformatics and Remote Sensing, SGEM 2022
dc.event.location Albena
utb.event.state-en Bulgaria
utb.event.state-cs Bulharsko
dc.event.sdate 2022-07-04
dc.event.edate 2022-07-10
dc.type conferenceObject
dc.language.iso en
dc.publisher International Multidisciplinary Scientific Geoconference
dc.identifier.doi 10.5593/sgem2022/2.1/s07.12
dc.relation.uri https://epslibrary.at/sgem_jresearch_publication_view.php?page=view&editid1=8479
dc.relation.uri https://www.proquest.com/docview/2780928372/fulltextPDF/256BEF11C0274261PQ/1?accountid=15518
dc.subject artificial intelligence en
dc.subject discrete dynamic models en
dc.subject evolutionary algorithms en
dc.subject neural networks en
dc.subject predictive control en
dc.description.abstract Predictive process control is a method of regulation suitable for controlling various types of systems, which is based on the idea of using the prediction of future system behavior and its optimization. Normally, a system model is used to predict behavior, and therefore it is necessary for the correct function of predictive control to make its correct selection and determine its parameters so that the controlled system is described as accurately as possible. Another advantage of predictive control is the possibility of including signal restrictions directly in the controller. The result is the application of some elements of artificial intelligence in suitable areas of predictive control, especially the use of simple evolutionary algorithms in optimization and neural networks as nonlinear models. One of the chapters of the article describes the possibilities of using these elements. It is proved that in addition to classical optimization algorithms, it is also possible to use simple evolutionary algorithms for optimization of prediction, while the computational complexity can be comparable depending on the type of solved problem and settings. The article describes a suitable selection of model systems with slow dynamics, their derivation, and the creation of nonlinear models in the form of scalable neural networks. The potential advantage of this approach for the control of systems that are difficult to describe or for the control of systems whose mathematical-physical description is not known. The chapter of the article also deals with the possibility of using the found models on real systems and determining the necessary conditions and requirements for their application. en
utb.faculty Faculty of Applied Informatics
dc.identifier.uri http://hdl.handle.net/10563/1011477
utb.identifier.obdid 43883610
utb.identifier.scopus 2-s2.0-85151059245
utb.source d-scopus
dc.date.accessioned 2023-04-11T13:35:55Z
dc.date.available 2023-04-11T13:35:55Z
dc.description.sponsorship Univerzita Tomáše Bati ve Zlíně: IGA / FAI / 2022/002
utb.contributor.internalauthor Blahová, Marta
utb.fulltext.sponsorship This research was based on the support of the Internal Grant Agency of Tomas Bata University in Zlín, the IGA/FAI/2022/002 project and the Department of Security Engineering, Faculty of Applied Informatics.
utb.scopus.affiliation Tomas Bata University in Zlín, Faculty of Applied Informatics, Czech Republic
utb.fulltext.projects IGA/FAI/2022/002
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