Full Text:   <1355>

CLC number: O344.3

On-line Access: 

Received: 2005-03-20

Revision Accepted: 2005-04-08

Crosschecked: 0000-00-00

Cited: 39

Clicked: 3149

Citations:  Bibtex RefMan EndNote GB/T7714

-   Go to

Article info.
1. Reference List
Open peer comments

Journal of Zhejiang University SCIENCE A 2006 Vol.7 No.3 P.378~382

10.1631/jzus.2006.A0378


Neural network method for solving elastoplastic finite element problems


Author(s):  Ren Xiao-qiang, Chen Wu-jun, Dong Shi-lin, Wang Feng

Affiliation(s):  Space Structures Research Center, Shanghai Jiao Tong University, Shanghai 200030, China

Corresponding email(s):   renxiaoqiang@sjtu.edu.cn

Key Words:  Elastoplasticity, Finite element method (FEM), Neural network


Ren Xiao-qiang, Chen Wu-jun, Dong Shi-lin, Wang Feng. Neural network method for solving elastoplastic finite element problems[J]. Journal of Zhejiang University Science A, 2006, 7(3): 378~382.

@article{title="Neural network method for solving elastoplastic finite element problems",
author="Ren Xiao-qiang, Chen Wu-jun, Dong Shi-lin, Wang Feng",
journal="Journal of Zhejiang University Science A",
volume="7",
number="3",
pages="378~382",
year="2006",
publisher="Zhejiang University Press & Springer",
doi="10.1631/jzus.2006.A0378"
}

%0 Journal Article
%T Neural network method for solving elastoplastic finite element problems
%A Ren Xiao-qiang
%A Chen Wu-jun
%A Dong Shi-lin
%A Wang Feng
%J Journal of Zhejiang University SCIENCE A
%V 7
%N 3
%P 378~382
%@ 1673-565X
%D 2006
%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.2006.A0378

TY - JOUR
T1 - Neural network method for solving elastoplastic finite element problems
A1 - Ren Xiao-qiang
A1 - Chen Wu-jun
A1 - Dong Shi-lin
A1 - Wang Feng
J0 - Journal of Zhejiang University Science A
VL - 7
IS - 3
SP - 378
EP - 382
%@ 1673-565X
Y1 - 2006
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/jzus.2006.A0378


Abstract: 
A basic optimization principle of Artificial neural network—the Lagrange Programming neural network (LPNN) model for solving elastoplastic finite element problems is presented. The nonlinear problems of mechanics are represented as a neural network based optimization problem by adopting the nonlinear function as nerve cell transfer function. Finally, two simple elastoplastic problems are numerically simulated. LPNN optimization results for elastoplastic problem are found to be comparable to traditional Hopfield neural network optimization model.

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article

Reference

[1] Gao, H.S., Li, H.D., Ye, T.L., 2000. Neurocomputing in structural analysis and design: An overview. Chinese Journal of Computational Mechanics, 17(2):223-228 (in Chinese).

[2] Sun, D.H., Hu, Q., Xu, H., 1998. Real time neurocomputing theory and numerical simulation on elastic mechanics. Acta Mechanica Sinica, 30(3):348-352.

[3] Sun, D.H., Sun, X.F., Hu, Q., 2000. Model for solving the elastoplasticity based on neural networks. Chinese Journal of Computational Mechanics, 17(3):273-277 (in Chinese).

[4] Wu, A.I., Tam, P.K.S., 1999. A neural network methodology and strategy of quadratic optimiSation. Neural Comput. & Applic., 8(4):283-289.

[5] Zhang, S.W., Constantinides, A.G., 1992. Lagrange programming neural networks. IEEE Trans. on Circuits and Systems II Analog and Digital Signal Processing, 39(7):441-452.

[6] Zhong, W.X., Zhang, H.W., Wu, C.W., 1997. Parametric Variation Principle and Applications in Engineering. Science Press, Beijing, p.1-123 (in Chinese).

Open peer comments: Debate/Discuss/Question/Opinion

<1>

Please provide your name, email address and a comment





Journal of Zhejiang University-SCIENCE, 38 Zheda Road, Hangzhou 310027, China
Tel: +86-571-87952783; E-mail: cjzhang@zju.edu.cn
Copyright © 2000 - Journal of Zhejiang University-SCIENCE