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Indexed by:期刊论文

Date of Publication:2009-12-01

Journal:INVERSE PROBLEMS IN SCIENCE AND ENGINEERING

Included Journals:SCIE、EI、Scopus

Volume:17

Issue:8

Page Number:999-1017

ISSN No.:1741-5977

Key Words:bimodularity; inverse problem; artificial neural network

Abstract: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.