• DocumentCode
    2740601
  • Title

    Dam´s Safety Monitoring Statistical Model Optimization Basing on The GA and AIC

  • Author

    Wu, Xinmiao ; Qie, Zhihong ; Liu, Hongquan ; Furuta, Hitoshi

  • Author_Institution
    Tianjin Univ.
  • Volume
    2
  • fYear
    0
  • fDate
    0-0 0
  • Firstpage
    7855
  • Lastpage
    7859
  • Abstract
    It is difficult to select influence factors when the dam´s monitoring model is built, so the Akaike information criterion (AIC) used in the field of information statistics is introduced. Both the fitting to modeling data and prediction precision to other data are considered in the AIC formula. The optimization method basing on GA and AIC is introduced. The method is applied to practical engineering, and the comparison with multiple regression, stepwise regression and neural network model shows the monitoring model optimized by the method can reach higher fitting and prediction precision by lesser factors and data
  • Keywords
    dams; genetic algorithms; monitoring; power engineering computing; safety systems; statistical analysis; Akaike information criterion; dam safety monitoring; data fitting; data modeling; data prediction precision; genetic algorithm; information statistics; statistical model optimization; Agriculture; Electronic mail; Fitting; Informatics; Minimax techniques; Monitoring; Optimization methods; Predictive models; Safety; Statistics; AIC; Genetic Algorithm; dam; monitoring model; optimization;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Intelligent Control and Automation, 2006. WCICA 2006. The Sixth World Congress on
  • Conference_Location
    Dalian
  • Print_ISBN
    1-4244-0332-4
  • Type

    conf

  • DOI
    10.1109/WCICA.2006.1713499
  • Filename
    1713499