Application of neural networks to the modelling of some constitutive laws

被引：30

作者：

Pernot, S ^{[1
]}

Lamarque, CH ^{[1
]}

机构：

[1] Ecole Natl Travaux Publ Etat, DGCB, LGM, URA CNRS 1652, F-69518 Vaulx En Velin, France

来源：

NEURAL NETWORKS | 1999年 / 12卷 / 02期

关键词：

constitutive laws; materials; neural networks; perceptron; modelling; backpropagation algorithm;

D O I：

10.1016/S0893-6080(98)00115-4

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This study investigates the modelling of constitutive laws of materials by neural networks. Material behaviour is no longer represented mathematically but is described by neuronal modelling. The main aim is to build a neural network directly from experimental results (the learning phase). We give several examples of constitutive laws (Hooke, Sargin, etc.) using a backpropagation algorithm. Then we show that abilities of adjustment, memorisation and anticipation of neural networks permit us to develop a method of classification of constitutive laws. (C) 1999 Elsevier Science Ltd. All rights reserved.

引用

页码：371 / 392

页数：22

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