• DocumentCode
    2325309
  • Title

    Volterra Series-based Neural Network and its Application in Tap-water Flow Forcast

  • Author

    Kun, Chen ; Lixiong, Li

  • Author_Institution
    Sch. of Mechatron. Eng. & Autom., Shanghai Univ., Shanghai, China
  • Volume
    1
  • fYear
    2011
  • fDate
    28-30 Oct. 2011
  • Firstpage
    97
  • Lastpage
    100
  • Abstract
    The system of community tap-water is influenced by many factors, which is a typical nonlinear dynamic system. Both neural networks and Volterra series are widely used in nonlinear dynamic system. This paper discusses the relations between Volterra series and BP neural network, and proposes the Volterra series-based neural network and the solution of the hight order Volterra series kernel. In this paper, the ARMA model, BP neural network and Volterra series-based neural network are applied to short-term forecast a community tap-water flows. According to the results of the comparison, it shows that the Volterra series-based neural network is better than other methods.
  • Keywords
    Volterra series; autoregressive moving average processes; backpropagation; forecasting theory; neural nets; water supply; ARMA model; BP neural network; Volterra series kernel; Volterra series-based neural network; community tap-water system; nonlinear dynamic system; short-term forecast; tap-water flow forcast; Analytical models; Biological neural networks; Communities; Equations; Mathematical model; Predictive models; Time series analysis; BP neural network; Volterra series; time series; water flow;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Computational Intelligence and Design (ISCID), 2011 Fourth International Symposium on
  • Conference_Location
    Hangzhou
  • Print_ISBN
    978-1-4577-1085-8
  • Type

    conf

  • DOI
    10.1109/ISCID.2011.33
  • Filename
    6079576