PARAMETERS INVERSION OF FLUID-SATURATED POROUS MEDIA BASED ON NEURAL NETWORKS

作     者：Wei Peijun Zhang Zimao Han Hua 

作者机构：Institute of MechanicsNorthern Jiaotong UniversityBeijing 100044China LNMInstitute of MechanicsChinese Academy of SciencesBeijing 100080China 

出 版 物：《Acta Mechanica Solida Sinica》 (固体力学学报（英文版）)

年 卷 期：2002年第15卷第4期

页      面：342-349页

核心收录：

学科分类：08[工学] 0805[工学-材料科学与工程（可授工学、理学学位）] 080502[工学-材料学] 0802[工学-机械工程] 0701[理学-数学] 0801[工学-力学（可授工学、理学学位）] 0702[理学-物理学] 

基　　金：the National Natural Science Foundation of China (Nos.19872002 and 10272003) Climbing Foundation of Northern Jiaotong University 

主　　题：fluid-saturated porous media parameter inversion, neural networks boundary elements 

摘      要：The multi- layers feedforward neural network is used for inversion ofmaterial constants of fluid-saturated porous media. The direct analysis of fluid-saturated porousmedia is carried out with the boundary element method. The dynamic displacement responses obtainedfrom direct analysis for prescribed material parameters constitute the sample sets training neuralnetwork. By virtue of the effective L-M training algorithm and the Tikhonov regularization method aswell as the GCV method for an appropriate selection of regu-larization parameter, the inversemapping from dynamic displacement responses to material constants is performed. Numerical examplesdemonstrate the validity of the neural network method.