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dc.contributor.author | Girbés-Juan, Vicent![]() |
es_ES |
dc.contributor.author | Moll, Joaquín![]() |
es_ES |
dc.contributor.author | Sala, Antonio![]() |
es_ES |
dc.contributor.author | Armesto, Leopoldo![]() |
es_ES |
dc.date.accessioned | 2024-07-01T18:36:02Z | |
dc.date.available | 2024-07-01T18:36:02Z | |
dc.date.issued | 2023-08 | es_ES |
dc.identifier.uri | http://hdl.handle.net/10251/205605 | |
dc.description.abstract | [EN] In this paper, a procedure for experimental optimization under safety constraints, to be denoted as constraint-aware Bayesian Optimization, is presented. The basic ingredients are a performance objective function and a constraint function; both of them will be modeled as Gaussian processes. We incorporate a prior model (transfer learning) used for the mean of the Gaussian processes, a semi-parametric Kernel, and acquisition function optimization under chance-constrained requirements. In this way, experimental fine-tuning of a performance objective under experiment-model mismatch can be safely carried out. The methodology is illustrated in a case study on a line-follower application in a CoppeliaSim environment. | es_ES |
dc.description.sponsorship | This research was funded by MCIN/AEI/10.13039/501100011033, Agencia Estatal de Investigación (Spanish government), grant numbers PID2020-116585GB-I00 and PID2020-118071GB-I00. | es_ES |
dc.language | Inglés | es_ES |
dc.publisher | MDPI AG | es_ES |
dc.relation.ispartof | Sensors | es_ES |
dc.rights | Reconocimiento (by) | es_ES |
dc.subject | Bayesian optimization | es_ES |
dc.subject | Safety constraints | es_ES |
dc.subject | Experimental optimization | es_ES |
dc.subject | Gaussian processes | es_ES |
dc.subject | Chance-constrained optimization | es_ES |
dc.subject.classification | INGENIERIA DE SISTEMAS Y AUTOMATICA | es_ES |
dc.title | Cautious Bayesian Optimization: A Line Tracker Case Study | es_ES |
dc.type | Artículo | es_ES |
dc.identifier.doi | 10.3390/s23167266 | es_ES |
dc.relation.projectID | info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2020-116585GB-I00/ES/APRENDIZAJE, CONTROL OPTIMO Y PLANIFICACION BAJO INCERTIDUMBRE EN APLICACIONES INDUSTRIALES/ | es_ES |
dc.relation.projectID | info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2020-118071GB-I00/ES/APRENDIZAJE AUTOMATICO BIOINSPIRADO/ | es_ES |
dc.rights.accessRights | Abierto | es_ES |
dc.contributor.affiliation | Universitat Politècnica de València. Escuela Técnica Superior de Ingenieros Industriales - Escola Tècnica Superior d'Enginyers Industrials | es_ES |
dc.description.bibliographicCitation | Girbés-Juan, V.; Moll, J.; Sala, A.; Armesto, L. (2023). Cautious Bayesian Optimization: A Line Tracker Case Study. Sensors. 23(16). https://doi.org/10.3390/s23167266 | es_ES |
dc.description.accrualMethod | S | es_ES |
dc.relation.publisherversion | https://doi.org/10.3390/s23167266 | es_ES |
dc.type.version | info:eu-repo/semantics/publishedVersion | es_ES |
dc.description.volume | 23 | es_ES |
dc.description.issue | 16 | es_ES |
dc.identifier.eissn | 1424-8220 | es_ES |
dc.identifier.pmid | 37631802 | es_ES |
dc.identifier.pmcid | PMC10458219 | es_ES |
dc.relation.pasarela | S\510541 | es_ES |
dc.contributor.funder | AGENCIA ESTATAL DE INVESTIGACION | es_ES |
dc.contributor.funder | Agencia Estatal de Investigación | es_ES |