›› 2003, Vol. 24 ›› Issue (6): 1025-1028.

• Fundamental Theroy and Experimental Research • Previous Articles     Next Articles

Application of RBFNN model to inversion of percolation parameter

LIU Xian-shan1, SHE Cheng-xue1, ZHANG Li-jun2   

  1. 1. School of Water Resources and Hydropower, Wuhan University, Wuhan 430072, China; 2. Zhanghe Administration Bureau, Jingmen 448156, China
  • Received:2003-08-17 Online:2003-12-10 Published:2014-08-19

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Abstract: Aimed at some limitation of the traditional BP neural network, the radial basic function neural network (RBFNN) is brought forward. On the same convergence condition, RBFNN is compared with the neural network based on the traditional algorithm and the excellence of the former is explained, As an example, on the basis of the nonlinear characteristic of ANN and 3-D seepage flow FEM computation , inversion results of the dam percolation parameter are obtained. The seepage flow field is analyzed by using the inversion results; the forecasted water heads approach to the observed values, which illuminates the result is accurate; RBFNN applied to the back analysis is reliable.

Key words: RBFNN, back analysis, percolation coefficient

CLC Number: 

  • TV 223.6
  • Please send e-mail to pingzhou3@126.com if you would like to read full paper in English for free. Parts of our published papers have English translations.
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