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Please use this identifier to cite or link to this item: http://hdl.handle.net/1843/43013
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dc.creatorEugênio Monteiro da Silva Júniorpt_BR
dc.creatorChristian Dias Cabacinhapt_BR
dc.creatorRenato Dourado Maiapt_BR
dc.date.accessioned2022-07-07T13:38:51Z-
dc.date.available2022-07-07T13:38:51Z-
dc.date.issued2018-09-
dc.citation.volume152pt_BR
dc.citation.spage401pt_BR
dc.citation.epage408pt_BR
dc.identifier.doihttps://doi.org/10.1016/j.compag.2018.07.036pt_BR
dc.identifier.issn0168-1699pt_BR
dc.identifier.urihttp://hdl.handle.net/1843/43013-
dc.description.resumoThe Eucalyptus is the most cultivated kind of tree in Brazil because it has adapted to the climate and has great importance for the industry. In cultivated forests, the wood volume is essential information to the forest management. Therefore, that information must be estimated as precisely as possible. There are several descriptive mathematical models which were developed for that purpose. However, Computational Intelligence techniques have been used in order to facilitate that process and substitute the volume models. Sundry works have proposed the use of Artificial Neural Networks for wood volume estimation, but there is a type of neural network, the Radial Basis Function – RBF, that can be designed automatically by clustering algorithms. This work presents the application of RBF networks automatically generated by the cOptBees clustering algorithm in the estimation of Eucalyptus volume and compares the results to the MLP networks and the classic models at the same dataset. The cOptBees is a clustering algorithm inspired by the behavior of bees which allows the number of clusters to be found automatically. To evaluate the various factors that can influence the quality of the results provided by RBF, the tests consider three training algorithms, three activation functions and three heuristics to define the spread. Besides the RBF generated by cOptBees, were evaluated another two types of RBF: randomly and k-means generated. In the volume estimation, the results indicate that neural networks and classical equations are equivalent to each other when there is high availability of data. However, when there are few training samples, the classical models performed better. Nevertheless, RBF networks are a viable alternative due to its ease of configuration and generalization capability.pt_BR
dc.languageengpt_BR
dc.publisherUniversidade Federal de Minas Geraispt_BR
dc.publisher.countryBrasilpt_BR
dc.publisher.departmentICA - INSTITUTO DE CIÊNCIAS AGRÁRIASpt_BR
dc.publisher.initialsUFMGpt_BR
dc.relation.ispartofComputers and Electronics in Agriculturept_BR
dc.rightsAcesso Abertopt_BR
dc.subject.otherRedes neurais (Computação)pt_BR
dc.subject.otherManejo florestalpt_BR
dc.subject.otherInteligência computacionalpt_BR
dc.subject.otherAlgoritmopt_BR
dc.subject.otherAgrossilviculturapt_BR
dc.subject.otherEucaliptopt_BR
dc.titleBee-inspired RBF network for volume estimation of individual treespt_BR
dc.typeArtigo de Periódicopt_BR
dc.url.externahttps://www.sciencedirect.com/science/article/pii/S0168169918301510#!pt_BR
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