• DocumentCode
    2305096
  • Title

    Optimezed Rock Mass Strength Parameter via PLS-RBF Neutral Network

  • Author

    Ma, Sha ; Li, Bingli

  • Author_Institution
    North China Univ. of Water Resources & Electr. Power, Zhengzhou, China
  • fYear
    2011
  • fDate
    25-27 April 2011
  • Firstpage
    196
  • Lastpage
    199
  • Abstract
    The neutral network further development is restricted in the system to some extent. The 3 layers RBF neutral network has the ability that self-study and self-remember, but sometimes because of serious multi-correlation between the variables, and a few observations while many variables, there usually will result into paralyzing in study. The partial least square regression has its advantage of building the calculation model between the variables with strong multi-correlation, especially much effective on a few data and many variables. So a new and effective method-improved neutral network has been introduced. The neutral network based on the partial least square regression. The results of example show the improved method has a few calculations and high accuracy, and provide a new way for valuing the rock mass strength parameters. Its network has been applied extensively.
  • Keywords
    correlation methods; least squares approximations; radial basis function networks; regression analysis; rocks; PLS-RBF neutral network; multicorrelation method; optimezed rock mass strength parameter; partial least square regression; Artificial neural networks; Computational modeling; Correlation; Data models; Fitting; Rocks; RBF neutral network; partial least square regression; rock mass strength parameter;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Information and Computing (ICIC), 2011 Fourth International Conference on
  • Conference_Location
    Phuket Island
  • Print_ISBN
    978-1-61284-688-0
  • Type

    conf

  • DOI
    10.1109/ICIC.2011.89
  • Filename
    5954539