The use of artificial neural networks in analysing the nutritional ecology of Chrysomya megacephala (F.) (Diptera: Calliphoridae), compared with a statistical model

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dc.contributor.authorBianconi, André [UNESP]
dc.contributor.authorZuben, Claudio Jose Von [UNESP]
dc.contributor.authorSerapião, Adriane Beatriz De Souza [UNESP]
dc.contributor.authorGovone, José Silvio [UNESP]
dc.contributor.institutionUniversidade Estadual Paulista (Unesp)
dc.date.accessioned2016-04-01T18:45:10Z
dc.date.available2016-04-01T18:45:10Z
dc.date.issued2010
dc.description.abstractArtificial neural networks (ANNs) have been widely applied to the resolution of complex biological problems. An important feature of neural models is that their implementation is not precluded by the theoretical distribution shape of the data used. Frequently, the performance of ANNs over linear or non-linear regression-based statistical methods is deemed to be significantly superior if suitable sample sizes are provided, especially in multidimensional and non-linear processes. The current work was aimed at utilising three well-known neural network methods in order to evaluate whether these models would be able to provide more accurate outcomes in relation to a conventional regression method in pupal weight predictions of Chrysomya megacephala, a species of blowfly (Diptera: Calliphoridae), using larval density (i.e. the initial number of larvae), amount of available food and pupal size as input data. It was possible to notice that the neural networks yielded more accurate performances in comparison with the statistical model (multiple regression). Assessing the three types of networks utilised (Multi-layer Perceptron, Radial Basis Function and Generalised Regression Neural Network), no considerable differences between these models were detected. The superiority of these neural models over a classical statistical method represents an important fact, because more accurate models may clarify several intricate aspects concerning the nutritional ecology of blowflies.en
dc.description.affiliationUniversidade Estadual Paulista Júlio de Mesquita Filho, Departamento de Estatística Matemática Aplicada E Computação, Instituto de Geociências e Ciências Exatas de Rio Claro, Rio Claro, av 24 A, 1515, Bela Vista, CEP 13506-970, SP, Brasil
dc.description.affiliationUnespUniversidade Estadual Paulista Júlio de Mesquita Filho, Departamento de Estatística Matemática Aplicada E Computação, Instituto de Geociências e Ciências Exatas de Rio Claro, Rio Claro, av 24 A, 1515, Bela Vista, CEP 13506-970, SP, Brasil
dc.format.extent201-212
dc.identifierhttp://onlinelibrary.wiley.com/doi/10.1111/j.1440-6055.2010.00754.x/abstract
dc.identifier.citationAustralian Journal of Entomology, v. 49, p. 201-212, 2010.
dc.identifier.issn1326-6756
dc.identifier.lattes534874933112053
dc.identifier.lattes6997814343189860
dc.identifier.lattes7562851016795381
dc.identifier.orcid0000-0001-9728-7092
dc.identifier.orcid0000-0002-9622-3254
dc.identifier.urihttp://hdl.handle.net/11449/137319
dc.language.isoeng
dc.relation.ispartofAustralian Journal of Entomology
dc.rights.accessRightsAcesso restrito
dc.sourceCurrículo Lattes
dc.subjectBlowflyen
dc.subjectLarval densityen
dc.subjectMass rearingen
dc.subjectNeural algorithmen
dc.subjectPupal weighten
dc.titleThe use of artificial neural networks in analysing the nutritional ecology of Chrysomya megacephala (F.) (Diptera: Calliphoridae), compared with a statistical modelen
dc.typeArtigo
unesp.author.lattes534874933112053
unesp.author.lattes6997814343189860[3]
unesp.author.lattes7562851016795381[2]
unesp.author.orcid0000-0001-9728-7092[3]
unesp.author.orcid0000-0002-9622-3254[2]
unesp.campusUniversidade Estadual Paulista (Unesp), Instituto de Geociências e Ciências Exatas, Rio Claropt
unesp.departmentEstatística Matemática Aplicada E Computaçãopt

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Artigos - Estatistica, Matemática Aplicada e Computação - IGCE