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A Neuro-Evolutive Interval Type-2 TSK Fuzzy System for Volatile Weather Forecasting

dc.contributor.authorKalenatic, Dusko
dc.contributor.authorFigueroa García, Juan Carlos
dc.contributor.authorLópez, Cesar Amílcar
dc.date.accessioned8/30/2019 10:07
dc.date.available8/30/2019 10:07
dc.date.issued2010
dc.identifier.otherhttps://link.springer.com/chapter/10.1007/978-3-642-14922-1_19
dc.identifier.urihttp://hdl.handle.net/10818/36964
dc.description8 páginases_CO
dc.description.abstractThis paper presents an hybrid Neuro-Evolutive algorithm for a First-order Interval Type-2 TSK Fuzzy Logic System applied to a volatile weather forecasting case. All results are tested by statistical tests as Goldfeld-Quant, Ljung-Box, ARCH, Runs, Turning Points, Bayesian, Akaike and Hannan-Quin criteria. Some methodological aspects about a hybrid implementation among ANFIS, an Evolutive Optimizer and a First order Interval Type-2 TSK FLS are presented. The selected type-reduction algorithm is the IASCO algorithm proposed by Melgarejo in [1] since it presents better computing properties than other algorithms.en
dc.formatapplication/pdfes_CO
dc.language.isoenges_CO
dc.publisherInternational Conference on Intelligent Computinges_CO
dc.relation.ispartofseriesAdvanced Intelligent Computing Theories and Applications pp 142-149
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.sourceUniversidad de La Sabanaes_CO
dc.sourceIntellectum Repositorio Universidad de La Sabanaes_CO
dc.subjectFuzzy Logic Systemen
dc.subjectMean Square Error Criterionen
dc.subjectNorth American Fuzzy Information Processingen
dc.subjectAmerican Fuzzy Information Processing Societyen
dc.subjectVolatile Time Seriesen
dc.titleA Neuro-Evolutive Interval Type-2 TSK Fuzzy System for Volatile Weather Forecastingen
dc.typejournal articlees_CO
dc.type.hasVersionpublishedVersiones_CO
dc.rights.accessRightsopenAccesses_CO
dc.identifier.doi10.1007/978-3-642-14922-1_19


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