Int J Neural Syst - Unorganized machines for seasonal streamflow series forecasting.

Tópicos

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{ time(1939) patient(1703) rate(768) }
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Resumo

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.

Resumo Limpo

modern unorgan machinesextrem learn machin echo state networksprovid eleg balanc process capabl mathemat simplic circumv difficulti associ convent train approach feedforwardrecurr neural network fnnsrnns work perform detail investig applic unorgan architectur problem season streamflow seri forecast consid scenario associ four brazilian hydroelectr plant four distinct predict horizon experiment result indic pertin model focus task

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