Volume 43 Issue 7
Jul.  2022
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WU Yucheng, YIN Hong, PENG Zhenrui. Stochastic Model Updating Based on Kriging Model and Lifting Wavelet Transform[J]. Applied Mathematics and Mechanics, 2022, 43(7): 761-771. doi: 10.21656/1000-0887.420128
Citation: WU Yucheng, YIN Hong, PENG Zhenrui. Stochastic Model Updating Based on Kriging Model and Lifting Wavelet Transform[J]. Applied Mathematics and Mechanics, 2022, 43(7): 761-771. doi: 10.21656/1000-0887.420128

Stochastic Model Updating Based on Kriging Model and Lifting Wavelet Transform

doi: 10.21656/1000-0887.420128
  • Received Date: 2021-05-10
  • Rev Recd Date: 2021-06-17
  • Publish Date: 2022-07-15
  • In order to improve the efficiency of stochastic model updating and reduce the amount of calculation, a stochastic model updating method based on Kriging model and lifting wavelet transform was proposed. Firstly, the lifting wavelet transform was performed on the acceleration frequency response function, and the 5th-level approximate coefficients were extracted to replace the original frequency response function; secondly, the Latin hypercube sampling was applied to sample the parameters to be updated and the corresponding approximate coefficients as the outputs to build the Kriging model. A butterfly optimization algorithm with Lévy flight (LBOA) was proposed and used to improve the accuracy of  Kriging model; finally, with the goal of minimizing the Wasserstein distance, the mean values of the parameters to be updated were solved with the whale optimization algorithm. The results of the test function show that, the LBOA greatly improves in terms of optimization, convergence accuracy and stability. The updating errors of the numerical examples are all less than 0.4%, and indicate the high accuracy and efficiency of the proposed model updating method.

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