• DocumentCode
    2351647
  • Title

    Self-organizing modeling in forecasting daily river flows

  • Author

    Valenga, M. ; Ludermir, Teresa

  • Author_Institution
    Companhia Hidro-Eletrica do Sao Francisco, Recife, Brazil
  • fYear
    1998
  • fDate
    9-11 Dec 1998
  • Firstpage
    210
  • Lastpage
    214
  • Abstract
    In a new approach, which corresponds in a better way to the actions of human nervous system, the connections between several neurons are not fixed but change in dependence on the neurons themselves. This article presents a GMDH (group method of data handling) algorithm with active neurons. These neurons are able, during the learning or self-organizing process, to estimate which inputs are important to minimize the given objective function of the neuron. The nonlinear GMDH model approach is shown to provide better representation of the daily average water inflow forecasting, than the models based on Box-Jenkins method, currently in use in the Brazilian Electrical Sector
  • Keywords
    forecasting theory; hydroelectric power stations; neural nets; rivers; Brazilian Electrical Sector; GMDH algorithm; active neurons; daily river flows; forecasting; group method of data handling; hydroelectric power station; neural nets; objective function; self-organizing modeling; Autoregressive processes; Dies; Mathematical model; Nervous system; Neurons; Power system modeling; Predictive models; Reservoirs; Rivers; Water resources;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Neural Networks, 1998. Proceedings. Vth Brazilian Symposium on
  • Conference_Location
    Belo Horizonte
  • Print_ISBN
    0-8186-8629-4
  • Type

    conf

  • DOI
    10.1109/SBRN.1998.731031
  • Filename
    731031