@article{LI2026, 
author = {Xiang LI and Zhinong JIANG and Huajin SHAO and Hao WANG and Yanfei ZUO},
title = {A neural network response surface updating method for complex dynamic models based on modal matching reconstruction strategy},
year = {2026},
journal = {Chinese Journal of Aeronautics},
volume = {39},
number = {5},
keywords = {Complex structures, Dynamic model updating, Modal analysis, Modal Assurance Criterion, Neural network, Response surface},
url = {https://www.sciopen.com/article/10.1016/j.cja.2025.103809},
doi = {10.1016/j.cja.2025.103809},
abstract = {An integrated dynamic model updating method is proposed to address the challenges of mode jumping and mode degeneracy for complex dynamic models. A reduced order proxy model of Neural Network Response Surface (NNRS) was constructed by Modal Matching Reconstruction Strategy (MMRS) and an Improved Vectorial Surrogate Model (IVSM). Among them, the analytical modes are correctly matched with the experimental modes by MMRS, and the order of the mode matching is determined by calculating the Modal Assurance Criterion (MAC), addressing the dynamic changes of the mode matching order during the construction of the NNRS. The fitted NNRS model results are vectorized by IVSM, enabling the rapid extraction of required input and output parameters under multi-parameter conditions, thereby improving efficiency. The model parameters are updated using a multi-objective genetic algorithm, which achieves the simultaneous updating of natural frequency and mode shape. To validate the accuracy and efficiency, an intermediate casing of a gas turbine was updated using the proposed method. With high efficiency, the mean absolute error of natural frequency for the matched order decreased from 24.46 % to 3.89 %, while the corresponding average MAC value increased from 0.654 to 0.752.}
}