dc.creatorJafelice, RM
dc.creatorAlmeida, C
dc.creatorMeyer, JFCA
dc.creatorVasconcelos, HL
dc.date2011
dc.dateDEC
dc.date2014-07-30T17:47:19Z
dc.date2015-11-26T16:51:55Z
dc.date2014-07-30T17:47:19Z
dc.date2015-11-26T16:51:55Z
dc.date.accessioned2018-03-28T23:38:45Z
dc.date.available2018-03-28T23:38:45Z
dc.identifierNonlinear Analysis-real World Applications. Pergamon-elsevier Science Ltd, v. 12, n. 6, n. 3397, n. 3412, 2011.
dc.identifier1468-1218
dc.identifierWOS:000295232900038
dc.identifier10.1016/j.nonrwa.2011.06.003
dc.identifierhttp://www.repositorio.unicamp.br/jspui/handle/REPOSIP/67725
dc.identifierhttp://repositorio.unicamp.br/jspui/handle/REPOSIP/67725
dc.identifier.urihttp://repositorioslatinoamericanos.uchile.cl/handle/2250/1276121
dc.descriptionConselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)
dc.descriptionThis paper proposes a model for the reoccupation of ants in a region of attraction, using evolutive partial differential diffusion-advection equations, in which the population dispersion and velocity in directions x and y are fuzzy parameters. The domain under study contains an attractive region, representing areas with high concentrations of palatable host plants with better nutritious qualities. The algorithm developed here uses information about the foraging behavior of a leaf-cutting ant colony of the Amazon region in northern Brazil. In the first model, we determined the numerical solution of the partial differential deterministic equation with constant dispersion and velocity, without incorporating uncertainties that may occur in this type of biological phenomenon. In the second model, we calculated the numerical solution of the partial differential equation, determining the dispersion at each iteration and for each triangular finite element. The dispersion was determined from a fuzzy rule-based system that depends on the number of individuals of the population and on the characteristics of the terrain. In this model, we also considered the velocity along the x and y directions as a fuzzy parameter that depends on the ant movement characteristics on the terrain in the y and x directions, respectively. The two models produced solutions consistent with ant behavior in response to the proximity of an attractive region, as stated in the papers by Shepherd (1982)[2] and Vasconcelos (1999)[3], but the fuzzy model incorporates uncertainties that do occur in the biological phenomenon under study. (C) 2011 Elsevier Ltd. All rights reserved.
dc.description12
dc.description6
dc.description3397
dc.description3412
dc.descriptionConselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)
dc.descriptionConselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)
dc.descriptionCNPq [152068/2007-4, 477918/2010-7]
dc.languageen
dc.publisherPergamon-elsevier Science Ltd
dc.publisherOxford
dc.publisherInglaterra
dc.relationNonlinear Analysis-real World Applications
dc.relationNonlinear Anal.-Real World Appl.
dc.rightsfechado
dc.rightshttp://www.elsevier.com/about/open-access/open-access-policies/article-posting-policy
dc.sourceWeb of Science
dc.subjectPartial differential equation
dc.subjectMathematical modeling
dc.subjectFuzzy set
dc.subjectNumerical methods
dc.subjectLeaf-cutting ants
dc.subjectLinguistic-synthesis
dc.subjectTrails
dc.titleFuzzy parameters in a partial differential equation model for population dispersal of leaf-cutting ants
dc.typeArtículos de revistas


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