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dc.contributor.authorBarragán, Antonio Javier-
dc.contributor.authorAl-Hadithi, Basil Mohammed-
dc.contributor.authorJiménez, Agustín-
dc.contributor.authorAndújar, José Manuel-
dc.date.issued2014-
dc.identifier.citationApplied Soft Computing Journal 18: 277- 289 (2014)-
dc.identifier.issn1568-4946-
dc.identifier.urihttp://hdl.handle.net/10261/102408-
dc.description.abstractThis paper presents an online TS fuzzy modeling general methodology based on the extended Kalman filter. The model can be obtained in a recursive way only based on input-output data. The methodology can work online with the system, properly in the presence of noise, is very efficient computationally and completely general. It is general in the sense theorically there are no restrictions neither in the number of inputs nor outputs, neither in the type nor distribution of membership functions used (which can even be mixed in the antecedents of the rules). Some examples and comparisons with other online fuzzy identification models from signals are provided to illustrate the skill of the online identification of the proposed methodology. © 2013 Elsevier B.V. All rights reserved.-
dc.description.sponsorshipSpanish Ministry of Education and Science. DPI2010-17123eng
dc.description.sponsorshipRegional Government of Andalusia (Spain). TEP-6124eng
dc.description.sponsorshipEuropean Union Regional Development Fundeng
dc.description.sponsorshipSpanish Ministry of Innovation and Science (ARABOT project). DPI2010-21247-C02-01eng
dc.publisherElsevier-
dc.rightsclosedAccess-
dc.subjectModeling-
dc.subjectKalman filter-
dc.subjectFuzzy system-
dc.subjectEstimation-
dc.subjectAlgorithm-
dc.titleA general methodology for online TS fuzzy modeling by the extended Kalman filter-
dc.typeartículo-
dc.identifier.doihttp://dx.doi.org/10.1016/j.asoc.2013.09.005-
dc.relation.publisherversionhttps://doi.org/10.1016/j.asoc.2013.09.005-
dc.date.updated2014-09-24T10:16:51Z-
dc.description.versionPeer Reviewed-
dc.language.rfc3066eng-
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