期刊
INTERNATIONAL JOURNAL OF NON-LINEAR MECHANICS
卷 94, 期 -, 页码 46-58出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.ijnonlinmec.2017.01.016
关键词
Elastic net; Lasso regression; Nonlinear identification; Data-driven
类别
资金
- Viterbi Postdoctoral Fellowship from the University of Southern California
This work discusses an improved method of reduced-order modeling for existing data-driven nonlinear identification techniques through the incorporation of naive elastic net regularization. The data-driven methods considered for this study operate using basis functions to represent the observed nonlinearity. Elastic net regularization is used to minimize the number of non-zero coefficients, thus modifying the basis functions and providing a compact representation. The ability of the naive elastic net to provide reduced-order nonlinear models that can both accurately fit various data sets and computationally simulate new responses is illustrated through studies considering both synthetic data and experimental data. In both cases, the results obtained with the naive elastic net are shown to match or outperform those from other traditional methods.
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