Dynamic model updating using neural networks
Abstract
The paper presents an application of Artificial Neural Networks for updating a mathematical model of the structure based on dynamic parameters. Neural networks which predict the value of selected stiffness or concentrated masses on the basis of Frequency Response Function (FRF) have been built. Two types of neural networks have been used for this task: multi-layer feed-forward (MLFF) networks with different learning algorithms and networks with radial basis function (RBF). Preceding the update, the FRF is compressed in order to reduce the number of input values necessary for updating the model.
References
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[4] R.I. Levin, N.A.J. Lieven. Dynamic finite element model updating using neural networks. Journal of Sound and Vibration, 210(5): 593- 607, 1998.
[5] R.I. Levin, N.A.J. Lieven. Dynamic finite element model updating using simulated annealing and genetic algorithms. Mechanical Systems and Signals Processing, 12(1): 91-120, 1998.
[2] M.I. Friswell, J .E. Motterhead. Finite Element Model Updating in Structural Dynamics. Kluwer Academic Publishers, Dordrecht, 1996.
[3] S. Haykin. Neural Networks: a Comprehensive Foundation, 2-nd ed. Prentice Hall International, Inc., 1999.
[4] R.I. Levin, N.A.J. Lieven. Dynamic finite element model updating using neural networks. Journal of Sound and Vibration, 210(5): 593- 607, 1998.
[5] R.I. Levin, N.A.J. Lieven. Dynamic finite element model updating using simulated annealing and genetic algorithms. Mechanical Systems and Signals Processing, 12(1): 91-120, 1998.
Published
2023-03-30
Issue
pp. 781-793
Section
Articles
License
Copyright © The Author(s).
This work is licensed under the Creative Commons Attribution 4.0 International CC BY 4.0.
How to Cite
Ziemiański, L., & Miller, B. (2023). Dynamic model updating using neural networks. Computer Assisted Methods in Engineering and Science, 7(4), 781-793. https://cames3.ippt.pan.pl/index.php/cames/article/view/1233
