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Type: Artigo de periódico
Author: Siqueira, H
Boccato, L
Attux, R
Lyra, C
Abstract: Modern unorganized machines - extreme learning machines and echo state networks - provide an elegant balance between processing capability and mathematical simplicity, circumventing the difficulties associated with the conventional training approaches of feedforward/recurrent neural networks (FNNs/RNNs). This work performs a detailed investigation of the applicability of unorganized architectures to the problem of seasonal streamflow series forecasting, considering scenarios associated with four Brazilian hydroelectric plants and four distinct prediction horizons. Experimental results indicate the pertinence of these models to the focused task.
Subject: Unorganized machines
extreme learning machines
echo state networks
seasonal streamflow series
Country: Singapura
Editor: World Scientific Publ Co Pte Ltd
Citation: International Journal Of Neural Systems. World Scientific Publ Co Pte Ltd, v. 24, n. 3, 2014.
Rights: fechado
Identifier DOI: 10.1142/S0129065714300095
Date Issue: 2014
Appears in Collections:Unicamp - Artigos e Outros Documentos

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