SciELO - Scientific Electronic Library Online

 
vol.38 issue2Neural networks for estimating of the volume of treesModeling of the diameter distribution for forest species in a fragment of mixed ombrophyllous forest author indexsubject indexarticles search
Home Pagealphabetic serial listing  

Services on Demand

Journal

Article

Indicators

Related links

Share


Revista Árvore

Print version ISSN 0100-6762

Abstract

GORGENS, Eric Bastos et al. Influence of the architecture in estimated volume of individual trees using artificial neural networks. Rev. Árvore [online]. 2014, vol.38, n.2, pp.289-295. ISSN 0100-6762.  https://doi.org/10.1590/S0100-67622014000200009.

Supervised neural networks are composed of parallel processing units. Each unit, called neurons, computes certain mathematical functions. The units are arranged in layers and connected by synaptic weights to balance the entries, trying to adjust them to a predetermined output pattern. The correct definition of the number of layers and the number of neurons in each layer are crucial, once the training is directly influenced by these parameters. To explore this point, data of scaling from five different regions were arranged in a spreadsheet and randomly divided into training and validation set. Data were presented for three networks with different architectures. The evaluation was performed using residual plots and t test (p <0.05). To estimate volume per tree, the neural network must be built with more than 10 neurons in the first layer, and it is recommended the use of more than one intermediate layer.

Keywords : Tree scaling; Neurons; Layers.

        · abstract in Portuguese     · text in Portuguese     · Portuguese ( pdf )

 

Creative Commons License All the contents of this journal, except where otherwise noted, is licensed under a Creative Commons Attribution License