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Parameter identification of dynamic models using a Bayes approach

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Abstract

The Bayesian method of statistical analysis has been applied to the parameter identification problem. A method is presented to identify parameters of dynamic models with the Bayes estimators of measurement frequencies. This is based on the solution of an inverse generalized eigenvalue problem. The stochastic nature of test data is considered and a normal distribution is used for the measurement frequencies. An additional feature is that the engineer's confidence in the measurement frequencies is quantified and incorporated into the identification procedure. A numerical example demonstrates the efficiency of the method.

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Authors and Affiliations

  1. Institute of Aircraft Design, Beijing University of Aeronautics & Astronautics, 100083, Beijing, P R China

    Li Shu Associate Professor, Doctor

  2. Institute of Civil Engineering, Hehai University, 210098, Nanjing, P R China

    Zhuo Jiashou & Ren Qingwen

Authors
  1. Li Shu Associate Professor, Doctor
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  2. Zhuo Jiashou
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  3. Ren Qingwen
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Additional information

Communicated by Ye Qingkai

Biography: Li Shu (1965-)

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Shu, L., Jiashou, Z. & Qingwen, R. Parameter identification of dynamic models using a Bayes approach. Appl Math Mech 21, 447–454 (2000). https://doi.org/10.1007/BF02463767

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  • DOI: https://doi.org/10.1007/BF02463767

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