2018/07/17 by Atiye Alaeddini, Alaeddini, Atiye, Siavash Alemzadeh +5
Computer Science · Engineering · Mathematics · #Control Systems and Identification #FOS: Mathematics #Gaussian Processes and Bayesian Inference #Optimization and Control (math.OC) #Statistical and numerical algorithms #math.OC
paper · pdf · doi:10.48550/arxiv.1807.06611
Accepted to 57th IEEE Conference on Decision and Control, 2018
openalex publication_date 2018/07/17 · arxiv created 2018/09/20 · arxiv updated 2018/09/24 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Data-driven methods for modeling dynamic systems have received considerable attention as they provide a mechanism for control synthesis directly from the observed time-series data. In the absence of prior assumptions on how the time-series had been generated, regression on the system model has been particularly popular. In the linear case, the resulting least squares setup for model regression, not only provides a computationally viable method to fit a model to the data, but also provides useful insights into the modal properties of the underlying dynamics. Although probabilistic estimates for this model regression have been reported, deterministic error bounds have not been examined in the literature, particularly as they pertain to the properties of the underlying system. In this paper, we provide deterministic non-asymptotic error bounds for fitting a linear model to the observed time-series data, with a particular attention to the role of symmetry and eigenvalue multiplicity in the underlying system matrix.