Unorganized machines for seasonal streamflow series forecasting
R. Attux, C. Lyra, H. Siqueira, L. Boccato
ARTIGO
Inglês
Agradecimentos: This work was supported by grants from CAPES,CNPq and FAPESP
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...
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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
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COORDENAÇÃO DE APERFEIÇOAMENTO DE PESSOAL DE NÍVEL SUPERIOR - CAPES
FUNDAÇÃO DE AMPARO À PESQUISA DO ESTADO DE SÃO PAULO - FAPESP
CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICO - CNPQ
Fechado
Unorganized machines for seasonal streamflow series forecasting
R. Attux, C. Lyra, H. Siqueira, L. Boccato
Unorganized machines for seasonal streamflow series forecasting
R. Attux, C. Lyra, H. Siqueira, L. Boccato
Fontes
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International journal of neural systems Vol. 24, no. 3 (May, 2014), n. art. 1430009 |