Simultaneous Regression and Selection in Nonlinear Modal Model Identification
Abstract
:1. Introduction
2. Theory
2.1. Nonlinear Dynamic Equations of Motion
2.2. Generating Static Force-Displacement Data
2.3. Estimating the Nonlinear Stiffness Terms
2.3.1. Least Squares Estimator
2.3.2. Least Absolute Shrinkage and Selection Operator (LASSO)
2.3.3. Repeated K-Fold Cross-Validation and Hyper-Parameter Selection
2.3.4. Discussion on Computational Cost
3. Numerical Examples
3.1. Flat Beam
3.1.1. ROM Training and Nonlinear Stiffness Terms
3.1.2. Evaluation of Accuracy
3.2. Curved Panel
3.2.1. Training and Nonlinear Stiffness Identification
3.2.2. Dynamic Accuracy Evaluation
4. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Conflicts of Interest
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Point | A | B | C |
---|---|---|---|
LS: All Load Cases | 0.0059 | 0.0070 | 0.0463 |
LS: Single Load 1xThk | 0.0079 | 0.0191 | NA |
LA: All Load Cases-Optimal | 0.0063 | 0.0075 | 0.0513 |
LA: All Load Cases- | 0.0093 | 0.0108 | 0.1074 |
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Van Damme, C.; Madrid, A.; Allen, M.; Hollkamp, J. Simultaneous Regression and Selection in Nonlinear Modal Model Identification. Vibration 2021, 4, 232-247. https://doi.org/10.3390/vibration4010016
Van Damme C, Madrid A, Allen M, Hollkamp J. Simultaneous Regression and Selection in Nonlinear Modal Model Identification. Vibration. 2021; 4(1):232-247. https://doi.org/10.3390/vibration4010016
Chicago/Turabian StyleVan Damme, Christopher, Alecio Madrid, Matthew Allen, and Joseph Hollkamp. 2021. "Simultaneous Regression and Selection in Nonlinear Modal Model Identification" Vibration 4, no. 1: 232-247. https://doi.org/10.3390/vibration4010016