A new identification method for use in nonlinear prediction

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dc.contributor.authorMontoya, F
dc.contributor.authorCipriano, A
dc.contributor.authorRamos, M
dc.date.accessioned2024-01-10T13:45:49Z
dc.date.available2024-01-10T13:45:49Z
dc.date.issued2001
dc.description.abstractThis paper presents a new identification method for fuzzy models used in nonlinear prediction. The structure and parameters of the fuzzy model are obtained, using input-output data, by minimization of the prediction error. The predictive capacity of the fuzzy model is compared with other linear and non-linear models analyzing an illustrative example. The results show that the new method presents a better behavior.
dc.format.extent7 páginas
dc.fuente.origenWOS
dc.identifier.eissn1875-8967
dc.identifier.issn1064-1246
dc.identifier.urihttps://repositorio.uc.cl/handle/11534/79084
dc.identifier.wosidWOS:000179475900001
dc.information.autorucIngeniería;Cipriano A;S/I;99102
dc.issue.numero3-4
dc.language.isoen
dc.nota.accesoSin adjunto
dc.pagina.final137
dc.pagina.inicio131
dc.publisherIOS PRESS
dc.revistaJOURNAL OF INTELLIGENT & FUZZY SYSTEMS
dc.rightsregistro bibliográfico
dc.subjectFUZZY
dc.subject.ods11 Sustainable Cities and Communities
dc.subject.odspa11 Ciudades y comunidades sostenibles
dc.titleA new identification method for use in nonlinear prediction
dc.typeartículo
dc.volumen10
sipa.codpersvinculados99102
sipa.indexWOS
sipa.trazabilidadCarga SIPA;09-01-2024
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