کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
499262 863035 2008 14 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Probabilistic model identification of uncertainties in computational models for dynamical systems and experimental validation
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر نرم افزارهای علوم کامپیوتر
پیش نمایش صفحه اول مقاله
Probabilistic model identification of uncertainties in computational models for dynamical systems and experimental validation
چکیده انگلیسی

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.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Computer Methods in Applied Mechanics and Engineering - Volume 198, Issue 1, 15 November 2008, Pages 150–163
نویسندگان
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