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ML models for real-time hybrid systems
dc.contributor.author | Capel, Manuel I. | |
dc.date.accessioned | 2021-08-26T09:58:41Z | |
dc.date.available | 2021-08-26T09:58:41Z | |
dc.date.issued | 2021 | |
dc.identifier.citation | Capel, M.I. ML models for real-time hybrid systems. En XLII Jornadas de Automática: libro de actas. Castelló, 1-3 de septiembre de 2021 (pp. 752-759). DOI capítulo: https://doi.org/10.17979/spudc.9788497498043.752 DOI libro: https://doi.org/10.17979/spudc.9788497498043 | es_ES |
dc.identifier.isbn | 978-84-9749-804-3 | |
dc.identifier.uri | http://hdl.handle.net/2183/28376 | |
dc.description.abstract | [Abstract] A correct system design can be systematically obtained from a specification model of a real-time system that integrates hybrid measurements In a realistic industrial environment, this has been carried out through complete Matlab / Simulink / Stateflow models. However, there is a widespread interest in carrying out that modeling resorting to Machine Learning models, which can be understood as Automated Machine Learning for Real-time systems that present some degree of hybridation. An AC motor controller which must be able to maintain a constant air flow through a filter is one of these systems. The article also discusses a practical application of the method for implementing a closed loop control system to show how the proposed procedure can be applied to derive complete hybrid system designs with ANN. | es_ES |
dc.language.iso | eng | es_ES |
dc.publisher | Universidade da Coruña, Servizo de Publicacións | es_ES |
dc.relation.uri | https://doi.org/10.17979/spudc.9788497498043.752 | es_ES |
dc.rights | Atribución-NoComercial-CompartirIgual 4.0 Internacional https://creativecommons.org/licenses/by-nc-sa/4.0/deed.es | es_ES |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-sa/3.0/es/ | * |
dc.subject | Automated machine learning | es_ES |
dc.subject | Realtime embedded control systems | es_ES |
dc.subject | Cyber-physical systems | es_ES |
dc.subject | Time series forecasting | es_ES |
dc.subject | Neural networks | es_ES |
dc.subject | Energy efficiency | es_ES |
dc.title | ML models for real-time hybrid systems | es_ES |
dc.type | info:eu-repo/semantics/conferenceObject | es_ES |
dc.rights.access | info:eu-repo/semantics/openAccess | es_ES |
UDC.startPage | 752 | es_ES |
UDC.endPage | 759 | es_ES |
dc.identifier.doi | https://doi.org/10.17979/spudc.9788497498043.752 | |
UDC.conferenceTitle | XLII Jornadas de Automática | es_ES |