Backpropagation and Levenberg-Marquardt Algorithm for Training Finite Element Neural Network

被引：62

作者：

Reynaldi, Arnold ^{[1
]}

Lukas, Samuel ^{[2
]}

Margaretha, Helena ^{[1
]}

机构：

[1] Pelita Harapan Univ, Fac Sci & Math, Tangerang, Banten, Indonesia

[2] Pelita Harapan Univ, Fac Comp Sci, Tangerang, Banten, Indonesia

来源：

2012 SIXTH UKSIM/AMSS EUROPEAN SYMPOSIUM ON COMPUTER MODELLING AND SIMULATION (EMS) | 2012年

关键词：

finite element method; artificial neural network; backpropagation algorithm; Levenberg-Marquardt algorithm; inverse differential problem;

D O I：

10.1109/EMS.2012.56

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

In this paper, finite element based neural network is developed. The purpose is to solve differential equation and inverse problem of differential equation. Inverse problem of differential equation is a problem to solve for parameters of differential equation, assuming that the solution of the differential equation is already known beforehand. Inverse problem mainly used to approximate physical parameters of material. Finite element method will be combined with artificial neural network using backpropagation algorithm to solve differential equation and Levenberg-Marquardt training algorithm to solve inverse differential problem. By using proposed method, invers matrix calculation will not be needed for solving both differential equation and inverse differential problem. From any given differential equation, the solution will be solved first. And the solution is used to validate the parameter in differential equation, namely to solve inverse problem of that differential equation.

引用

页码：89 / 94

页数：6

共 50 条

[1] Neighborhood based Levenberg-Marquardt algorithm for neural network training
Lera, G
Pinzolas, M
IEEE TRANSACTIONS ON NEURAL NETWORKS, 2002, 13 (05): : 1200 - 1203
[2] Modified Levenberg-Marquardt Algorithm for Backpropagation Neural Network Training in Dynamic Model Identification of Mechanical Systems
Li, Ming
Wu, Huapeng
Wang, Yongbo
Handroos, Heikki
Carbone, Giuseppe
JOURNAL OF DYNAMIC SYSTEMS MEASUREMENT AND CONTROL-TRANSACTIONS OF THE ASME, 2017, 139 (03):
[3] A quasi-local Levenberg-Marquardt algorithm for neural network training
Lera, G
Pinzolas, M
IEEE WORLD CONGRESS ON COMPUTATIONAL INTELLIGENCE, 1998, : 2242 - 2246
[4] Variable projection method and levenberg-marquardt algorithm for neural network training
Kim, Cheol-Taek
Lee, Ju-Jang
Kim, Hyejin
IECON 2006 - 32ND ANNUAL CONFERENCE ON IEEE INDUSTRIAL ELECTRONICS, VOLS 1-11, 2006, : 2084 - +
[5] A Novel Modification on the Levenberg-Marquardt Algorithm for Avoiding Overfitting in Neural Network Training
Iplikci, Serdar
Bilgi, Batuhan
Menemen, Ali
Bahtiyar, Bedri
ARTIFICIAL NEURAL NETWORKS AND MACHINE LEARNING - ICANN 2019: DEEP LEARNING, PT II, 2019, 11728 : 201 - 207
[6] Stability Analysis of the Modified Levenberg-Marquardt Algorithm for the Artificial Neural Network Training
Rubio, Jose de Jesus
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2021, 32 (08) : 3510 - 3524
[7] Application of backpropagation neural network based on levenberg-marquardt algorithm in detection of fraudulent financial statements
Deng Qingshan
Mei Guoping
ADVANCED COMPUTER TECHNOLOGY, NEW EDUCATION, PROCEEDINGS, 2007, : 151 - 154
[8] Parallel Levenberg-Marquardt Algorithm Without Error Backpropagation
Bilski, Jaroslaw
Wilamowski, Bogdan M.
ARTIFICIAL INTELLIGENCE AND SOFT COMPUTING, ICAISC 2017, PT I, 2017, 10245 : 25 - 39
[9] Adaptive Levenberg-Marquardt Algorithm: A New Optimization Strategy for Levenberg-Marquardt Neural Networks
Yan, Zhiqi
Zhong, Shisheng
Lin, Lin
Cui, Zhiquan
MATHEMATICS, 2021, 9 (17)
[10] Neural Network Training With Levenberg-Marquardt and Adaptable Weight Compression
Smith, James S.
Wu, Bo
Wilamowski, Bogdan M.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2019, 30 (02) : 580 - 587

← 1 2 3 4 5 →