Please use this identifier to cite or link to this item: http://repositoriosenaiba.fieb.org.br/handle/fieb/299
Title: A Model for improving the learning curves of artificial neural networks
Other Titles: Plos One
Authors: Monteiro, Roberto L. S.
Carneiro, Tereza Kelly G.
Fontoura, José Roberto A.
Silva, Valéria L. da
Keywords: Neural network - Performance;Artificial neural network;Neural network - learning curve
Issue Date: 22-Feb-2016
Citation: MONTEIRO, Roberto L. S. et al. A Model for Improving the Learning Curves of Artificial Neural Networks. Plos One , v. 11, p. e0149874, 2016.
Abstract: In this article, the performance of a hybrid artificial neural network (i.e. scale-free and smallworld) was analyzed and its learning curve compared to three other topologies: random, scale-free and small-world, as well as to the chemotaxis neural network of the nematode Caenorhabditis Elegans. One hundred equivalent networks (same number of vertices and average degree) for each topology were generated and each was trained for one thousand epochs. After comparing the mean learning curves of each network topology with the C. elegans neural network, we found that the networks that exhibited preferential attachment exhibited the best learning curves.
Description: p.1-11
URI: http://repositoriosenaiba.fieb.org.br/handle/fieb/299
Appears in Collections:Artigos Publicados em Periódicos (PPG MCTI)

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