期刊
COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING
卷 198, 期 1, 页码 150-163出版社
ELSEVIER SCIENCE SA
DOI: 10.1016/j.cma.2008.04.007
关键词
Identification; Optimization; Uncertain computational model; Structural dynamics; Vibroacoustics; Experimental validation
资金
- French National Research Agency (ANR)
- PSA Peugeot-Citroen
- Universite Paris-Est
- Marne la Vallee and Laboratoire d'Analyse des Materiaux et Identification (LAMI)
We present a methodology to perform the identification and validation of complex uncertain dynamical systems using experimental data, for which uncertainties are taken into account by using the nonparametric probabilistic approach. Such a probabilistic model of uncertainties allows both model uncertainties and parameter uncertainties to be addressed by using only a small number of unknown identification parameters. Consequently, the optimization problem which has to be solved in order to identify the unknown identification parameters from experiments is feasible. Two formulations are proposed. The first one is the mean-square method for which a usual differentiable objective function and an unusual non-differentiable objective function are proposed. The second one is the maximum likelihood method coupling with a statistical reduction which leads us to a considerable improvement of the method. Three applications with experimental validations are presented in the area of structural vibrations and vibroacoustics. (C) 2008 Elsevier B.V. All rights reserved
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