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论文类型：期刊论文

发表时间：2009-12-01

发表刊物：INVERSE PROBLEMS IN SCIENCE AND ENGINEERING

收录刊物：SCIE、EI、Scopus

卷号：17

期号：8

页面范围：999-1017

ISSN号：1741-5977

关键字：bimodularity; inverse problem; artificial neural network

摘要：This article suggests the utilization of artificial neural network to estimate bimodular constitutive parameters, including extensional/compressive moduli, and extensional/compressive Poisson's ratios. By combining a smoothing function with an initial stress scheme, solutions of the direct bimodular problems are provided by finite element (FE) techniques, and are employed as input to train the networks. One- and two-dimensional numerical examples are presented to illustrate the performance of the network, and good results are achieved. Several factors affecting network performance are discussed.