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dc.contributor.authorLiu, Xian-Xian
dc.contributor.authorHu, Shimin
dc.contributor.authorFong, Simon James
dc.contributor.authorGonzález-Crespo, Rubén (1)
dc.contributor.authorHerrera-Viedma, Enrique
dc.date2021
dc.date.accessioned2022-01-14T10:59:37Z
dc.date.available2022-01-14T10:59:37Z
dc.identifier.issn1478-3967
dc.identifier.urihttps://reunir.unir.net/handle/123456789/12308
dc.description.abstractIn this paper, we demonstrate the application of MATLAB to develop a pandemic prediction system based on Simulink. The susceptible-exposed-asymptomatic but infectious-symptomatic and infectious (severe infected population + mild infected population)-recovered-deceased (SEAI(I (1) + I (2))RD) physical model for unsupervised learning and two types of supervised learning, namely, fuzzy proportional-integral-derivative (PID) and wavelet neural-network PID learning, are used to build a predictive-control system model that enables self-learning artificial intelligence (AI)-based control. After parameter setting, the data entering the model are predicted, and the value of the data set at a future moment is calculated. PID controllers are added to ensure that the system does not diverge at the beginning of iterative learning. To adapt to complex system conditions and afford excellent control, a wavelet neural-network PID control strategy is developed that can be adjusted and corrected in real time, according to the output error.es_ES
dc.language.isoenges_ES
dc.publisherPhysical biologyes_ES
dc.relation.ispartofseries;vol. 18, nº 4
dc.relation.urihttps://iopscience.iop.org/article/10.1088/1478-3975/abf990es_ES
dc.rightsrestrictedAccesses_ES
dc.subjectnovel coronaviruses_ES
dc.subjectasymptomatic caseses_ES
dc.subjectprocess simulationes_ES
dc.subjectepidemiologyes_ES
dc.subjectSEAIRDes_ES
dc.subjectSimulinkes_ES
dc.subjectWOS(2)es_ES
dc.subjectScopuses_ES
dc.titleModelling dynamics of coronavirus disease 2019 spread for pandemic forecasting based on Simulinkes_ES
dc.typearticlees_ES
reunir.tag~ARIes_ES
dc.identifier.doihttps://doi.org/10.1088/1478-3975/abf990


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