Application of prediction models using fuzzy sets : a Bayesian inspired approach.

dc.contributor.authorBacani, Felipo
dc.contributor.authorBarros, Laécio Carvalho de
dc.date.accessioned2017-12-13T12:57:27Z
dc.date.available2017-12-13T12:57:27Z
dc.date.issued2017
dc.description.abstractA fuzzy inference framework based on fuzzy relations is developed, adapted and applied to temperature and humidity measure-ments from a specific coffee crop site in Brazil. This framework consists of fuzzy relations over possibility distributions, resulting in a model analogous to a Bayesian inference process. The application of this fuzzy model to a data set of experimental measurements and its correspondent forecasts of temperature and humidity resulted in a set of revised forecasts, that incorporate information from a historical record of the problem. Each set of revised forecasts was compared with the correspondent set of experimental data using two different statistical measures, MAPE (Mean Absolute Percentage Error) and Willmott’s D. This comparison showed that the sets of forecasts revised by the fuzzy model exhibited better results than the original forecasts on both statistical measures for more than two thirds of the evaluated cases.pt_BR
dc.identifier.citationBACANI, F.; BARROS, L. C. de. Application of prediction models using fuzzy sets: a Bayesian inspired approach. Fuzzy Sets and Systems, v. 319, p. 104-116, 2017. Disponível em: <http://www.sciencedirect.com/science/article/pii/S0165011416302792>. Acesso em: 02 out. 2017.pt_BR
dc.identifier.doihttps://doi.org/10.1016/j.fss.2016.09.008
dc.identifier.issn0165-0114
dc.identifier.urihttp://www.repositorio.ufop.br/handle/123456789/9220
dc.identifier.uri2http://www.sciencedirect.com/science/article/pii/S0165011416302792pt_BR
dc.language.isoen_USpt_BR
dc.rightsrestritopt_BR
dc.subjectPossibility theorypt_BR
dc.subjectFuzzy inference systemspt_BR
dc.subjectFuzzy relationspt_BR
dc.titleApplication of prediction models using fuzzy sets : a Bayesian inspired approach.pt_BR
dc.typeArtigo publicado em periodicopt_BR
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