Identification of Linear Time-Invariant Systems with Dynamic Mode Decomposition

نویسندگان

چکیده

Dynamic mode decomposition (DMD) is a popular data-driven framework to extract linear dynamics from complex high-dimensional systems. In this work, we study the system identification properties of DMD. We first show that DMD invariant under transformations in image data matrix. If, addition, are constructed time-invariant system, then prove can recover original mild conditions. If discretized with Runge–Kutta method, further classify error approximation and detail for one-stage methods; even continuous be recovered A numerical example illustrates theoretical findings.

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ژورنال

عنوان ژورنال: Mathematics

سال: 2022

ISSN: ['2227-7390']

DOI: https://doi.org/10.3390/math10030418