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

Página do item simplificado
dc.contributor.authorBianconi, Andre [UNESP]
dc.contributor.authorVon Zuben, Claudio J. [UNESP]
dc.contributor.authorde Souza Serapiao, Adriane B.
dc.contributor.authorGovone, Jose S.
dc.contributor.institutionUniversidade Estadual Paulista (Unesp)
dc.contributor.institutionDept Estat Matemat Aplicada & Comp
dc.date.accessioned2014-05-20T13:59:59Z
dc.date.available2014-05-20T13:59:59Z
dc.date.issued2010-01-01
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.affiliationSão Paulo State Univ, UNESP, Inst Biociencias, Dept Zool, BR-13506900 Rio Claro, SP, Brazil
dc.description.affiliationDept Estat Matemat Aplicada & Comp, Rio Claro, SP, Brazil
dc.description.affiliationUnespSão Paulo State Univ, UNESP, Inst Biociencias, Dept Zool, BR-13506900 Rio Claro, SP, Brazil
dc.description.sponsorshipFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
dc.description.sponsorshipConselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)
dc.format.extent201-212
dc.identifierhttp://dx.doi.org/10.1111/j.1440-6055.2010.00754.x
dc.identifier.citationAustralian Journal of Entomology. Malden: Wiley-blackwell, v. 49, p. 201-212, 2010.
dc.identifier.doi10.1111/j.1440-6055.2010.00754.x
dc.identifier.issn1326-6756
dc.identifier.lattes7562851016795381
dc.identifier.orcid0000-0002-9622-3254
dc.identifier.urihttp://hdl.handle.net/11449/21214
dc.identifier.wosWOS:000281211900001
dc.language.isoeng
dc.publisherWiley-Blackwell
dc.relation.ispartofAustralian Journal of Entomology
dc.rights.accessRightsAcesso restrito
dc.sourceWeb of Science
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.typeResumo
dcterms.licensehttp://olabout.wiley.com/WileyCDA/Section/id-406071.html
dcterms.rightsHolderWiley-blackwell
unesp.author.lattes7562851016795381[2]
unesp.author.orcid0000-0002-9622-3254[2]
unesp.author.orcid0000-0001-9728-7092[3]
unesp.campusUniversidade Estadual Paulista (Unesp), Instituto de Biociências, Rio Claropt

Arquivos

Licença do Pacote
Agora exibindo 1 - 2 de 2
Nenhuma Miniatura disponível
Nome:
license.txt
Tamanho:
1.71 KB
Formato:
Item-specific license agreed upon to submission
Descrição:
Nenhuma Miniatura disponível
Nome:
license.txt
Tamanho:
1.71 KB
Formato:
Item-specific license agreed upon to submission
Descrição:

Coleções

Artigos - Zoologia - IBRC