Predictive deep learning models for analyzing discrete fractional dynamics from noisy and incomplete data

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dc.authoridConejero, J. Alberto/0000-0003-3681-7533
dc.contributor.authorGaribo-i-Orts, Oscar
dc.contributor.authorLizama, Carlos
dc.contributor.authorAkgul, Ali
dc.contributor.authorConejero, J. Alberto
dc.date.accessioned2024-12-24T19:26:59Z
dc.date.available2024-12-24T19:26:59Z
dc.date.issued2024
dc.departmentSiirt Üniversitesi
dc.description.abstractWe study the accuracy of machine learning methods for inferring the parameters of noisy fractional Wu-Baleanu trajectories with some missing initial terms. Our model is based on a combination of convolutional and recurrent neural networks (LSTM), which permits the extraction of characteristics from trajectories while preserving time dependency. We show that these approach exhibit good accuracy results despite the poor quality of the data.
dc.description.sponsorshipSpanish Ministry of Science and Innovation (MICINN); MCIN/AEI [PID2021-124618NB-C21]; ERDF A way of making Europe; European Union; Agencia Nacional para la Innovacion y del Desarrollo (ANID); CRUE-Universitat Politecnica de Valencia; [1220036]
dc.description.sponsorshipJAC acknowledges funding from the Spanish Ministry of Science and Innovation (MICINN) , grant PID2021-124618NB-C21 funded by MCIN/AEI/10.13039/501100011033 and by ERDF A way of making Europe, by the European Union. CL is partially funded by Agencia Nacional para la Innovacion y del Desarrollo (ANID) , under FONDECYT grant number 1220036. We also acknowledge funding for open access charge: CRUE-Universitat Politecnica de Valencia.
dc.identifier.doi10.1016/j.cjph.2024.04.010
dc.identifier.endpage1285
dc.identifier.issn0577-9073
dc.identifier.scopus2-s2.0-85190886889
dc.identifier.scopusqualityQ1
dc.identifier.startpage1276
dc.identifier.urihttps://doi.org/10.1016/j.cjph.2024.04.010
dc.identifier.urihttps://hdl.handle.net/20.500.12604/6435
dc.identifier.volume89
dc.identifier.wosWOS:001233807100001
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherElsevier
dc.relation.ispartofChinese Journal of Physics
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_20241222
dc.subjectDynamical systems
dc.subjectDiscrete fractional calculus
dc.subjectWu-Baleanu model
dc.subjectLogistic map
dc.subjectConvolutional neural networks
dc.subjectLSTM networks
dc.titlePredictive deep learning models for analyzing discrete fractional dynamics from noisy and incomplete data
dc.typeArticle

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