• DocumentCode
    2662340
  • Title

    Study of prediction based on RBF Neural network optimized by PSO

  • Author

    Lianghai, Wu ; Yiming, Chen

  • Author_Institution
    Dept. of Exp. Teaching, Maoming Univ., Maoming, China
  • Volume
    6
  • fYear
    2010
  • fDate
    16-18 April 2010
  • Abstract
    The parameters of RBF neural network have important effect to its performance. The parameters selection is the important research content of the RBF Neural network. For this problem, this study proposed a kind of method to choose the parameters of the RBF neural network by particle swarm optimization algorithm (PSO). The experiment result indicates the RBF neural network prediction model optimized by PSO has high prediction accuracy, and PSO is one kind of effective method for RBF neural network parameters selection.
  • Keywords
    demand forecasting; particle swarm optimisation; petroleum; radial basis function networks; RBF neural network optimisation; particle swarm optimization algorithm; petroleum demand; prediction accuracy; Accuracy; Demand forecasting; Economic forecasting; Neural networks; Neurons; Optimization methods; Particle swarm optimization; Petroleum; Predictive models; Production; RBF neural network; particle swarm optimization; petroleum demand; prediction model;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Computer Engineering and Technology (ICCET), 2010 2nd International Conference on
  • Conference_Location
    Chengdu
  • Print_ISBN
    978-1-4244-6347-3
  • Type

    conf

  • DOI
    10.1109/ICCET.2010.5486107
  • Filename
    5486107