How entropic regression beats the outliers problem in nonlinear system identification

DOI10.1063/1.5133386zbMath1433.93032arXiv1905.08061OpenAlexW2997092692WikidataQ89512435 ScholiaQ89512435MaRDI QIDQ5218142

Jie Sun, Abd AlRahman R. AlMomani, Erik M. Bollt

Publication date: 28 February 2020

Published in: Chaos: An Interdisciplinary Journal of Nonlinear Science (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1905.08061

zbMATH Keywords

nonlinear system identificationentropic regression

Mathematics Subject Classification ID

System identification (93B30) Nonlinear systems in control theory (93C10) Measures of information, entropy (94A17)

Related Items (5)

SubTSBR to tackle high noise and outliers for data-driven discovery of differential equations ⋮ Sparse identification of dynamical systems by reweighted \(l_1\)-regularized least absolute deviation regression ⋮ The structure of reconstructed flows in latent spaces ⋮ Sparse methods for automatic relevance determination ⋮ Bridging the gap: machine learning to resolve improperly modeled dynamics

Uses Software

• CVX

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