Unifying Theorems for Subspace Identification and Dynamic Mode Decomposition
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Publication:6336858
arXiv2003.07410MaRDI QIDQ6336858
Author name not available (Why is that?)
Publication date: 16 March 2020
Abstract: This paper presents unifying results for subspace identification (SID) and dynamic mode decomposition (DMD) for autonomous dynamical systems. We observe that SID seeks to solve an optimization problem to estimate an extended observability matrix and a state sequence that minimizes the prediction error for the state-space model. Moreover, we observe that DMD seeks to solve a rank-constrained matrix regression problem that minimizes the prediction error of an extended autoregressive model. We prove that existence conditions for perfect (error-free) state-space and low-rank extended autoregressive models are equivalent and that the SID and DMD optimization problems are equivalent. We exploit these results to propose a SID-DMD algorithm that delivers a provably optimal model and that is easy to implement. We demonstrate our developments using a case study that aims to build dynamical models directly from video data.
Has companion code repository: https://github.com/zavalab/JuliaBox
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