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dc.creatorLeite, Daniel Furtado-
dc.creatorAndonovski, Goran-
dc.creatorŠkrjanc, Igor-
dc.creatorGomide, Fernando Antonio Campos-
dc.date.accessioned2020-08-13T18:13:41Z-
dc.date.available2020-08-13T18:13:41Z-
dc.date.issued2020-03-
dc.identifier.citationLEITE, D. et al. Optimal Rule-Based Granular Systems From Data Streams. IEEE Transactions on Fuzzy Systems, Piscataway, v. 28, n. 3, p. 583-596, Mar. 2020. DOI: 10.1109/TFUZZ.2019.2911493.pt_BR
dc.identifier.urihttps://ieeexplore.ieee.org/document/8691724/authors#authorspt_BR
dc.identifier.urihttp://repositorio.ufla.br/jspui/handle/1/42414-
dc.description.abstractWe introduce an incremental learning method for the optimal construction of rule-based granular systems from numerical data streams. The method is developed within a multiobjective optimization framework considering the specificity of information, model compactness, and variability and granular coverage of the data. We use α-level sets over Gaussian membership functions to set model granularity and operate with hyperrectangular forms of granules in nonstationary environments. The resulting rule-based systems are formed in a formal and systematic fashion. They can be useful in time series modeling, dynamic system identification, predictive analytics, and adaptive control. Precise estimates and enclosures are given by linear piecewise and inclusion functions related to optimal granular mappings.pt_BR
dc.languageen_USpt_BR
dc.publisherIEEE – Institute of Electrical and Electronic Engineerspt_BR
dc.rightsrestrictAccesspt_BR
dc.sourceIEEE Transactions on Fuzzy Systemspt_BR
dc.subjectGranular coverage of the datapt_BR
dc.subjectGaussian modelpt_BR
dc.subjectNonstationary environmentspt_BR
dc.subjectSistemas granularespt_BR
dc.subjectModelo gaussianopt_BR
dc.subjectAmbientes não-estacionáriospt_BR
dc.titleOptimal rule-based granular systems from data streamspt_BR
dc.typeArtigopt_BR
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