期刊
COMPUTERS & STRUCTURES
卷 124, 期 -, 页码 29-37出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.compstruc.2012.11.011
关键词
Recurrent neural networks; Fuzzy data; Model-free material description; Fuzzy structural analysis; Finite element method; alpha-Level optimization
资金
- Deutsche Forschungsgemeinschaft (DFG - German Research Foundation) [FR 3044/1-1]
A new soft computing approach is presented for structural analysis. Instead of material models, an artificial neural network concept is applied to describe time-dependent material behaviour within the finite element method. In order to consider imprecise data for the identification of dependencies between strain and stress processes from uncertain results of experimental investigations, recurrent neural networks for fuzzy data are used. An algorithm for the signal computation of recurrent neural networks is developed utilizing an alpha-level optimization. The approach is verified by a model based solution. Application capabilities are demonstrated by means of numerical examples. (C) 2012 Elsevier Ltd. All rights reserved.
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