Parameters inversion of fluid-saturated porous media based on neural networks

被引:0
|
作者
Wei, PJ [1 ]
Zhang, ZM
Han, H
机构
[1] Natl Jiaotong Univ, Inst Mech, Beijing 100044, Peoples R China
[2] Chinese Acad Sci, Inst Mech, LNM, Beijing 100080, Peoples R China
关键词
fluid-saturated porous media; parameter inversion; neural networks; boundary elements;
D O I
暂无
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
The multi-layers feedforward neural network is used for inversion of material constants of fluid-saturated porous media. The direct analysis of fluid-saturated porous media is carried out with the boundary element method. The dynamic displacement responses obtained from direct analysis for prescribed material parameters constitute the sample sets training neural network. By virtue of the effective L-M training algorithm and the Tikhonov regularization method as well as the GCV method for an appropriate selection of regularization parameter, the inverse mapping from dynamic displacement responses to material constants is performed. Numerical examples demonstrate the validity of the neural network method.
引用
收藏
页码:342 / 349
页数:8
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