On Reduced Input-Output Dynamic Mode Decomposition
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Abstract (arXiv)
The identification of reduced-order models from high-dimensional data is a challenging task, and even more so if the identified system should not only be suitable for a certain data set, but generally approximate the input-output behavior of the data source. In this work, we consider the input-output dynamic mode decomposition method for system identification. We compare excitation approaches for the data-driven identification process and describe an optimization-based stabilization strategy for the identified systems.
Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). First posted on arXiv 22 Dec 2017.
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Citing papers per year: 2021: 2; 2022: 1; 2023: 1.
4 papers.
- 2021Modern Koopman Theory for Dynamical Systemscited by 276
- 2022Generalized Quadratic Embeddings for Nonlinear Dynamics using Deep Learningcited by 9
- 2021Learning Low-Dimensional Quadratic-Embeddings of High-Fidelity Nonlinear Dynamics using Deep Learningcited by 2
- 2023Inference of Continuous Linear Systems from Data with Guaranteed Stabilitycited by 0
- 2023Inference of Continuous Linear Systems from Data with Guaranteed Stability24 Jan 2023
- 2022Generalized Quadratic Embeddings for Nonlinear Dynamics using Deep Learning1 Nov 2022
- 2021Learning Low-Dimensional Quadratic-Embeddings of High-Fidelity Nonlinear Dynamics using Deep Learning25 Nov 2021
- 2021Modern Koopman Theory for Dynamical Systems24 Feb 2021