System identification methods for (operational) modal analysis: review and comparison

E Reynders - Archives of Computational Methods in Engineering, 2012 - Springer
Operational modal analysis deals with the estimation of modal parameters from vibration
data obtained in operational rather than laboratory conditions. This paper extensively …

An overview of subspace identification

SJ Qin - Computers & chemical engineering, 2006 - Elsevier
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Finite sample analysis of stochastic system identification

A Tsiamis, GJ Pappas - … IEEE 58th Conference on Decision and …, 2019 - ieeexplore.ieee.org
In this paper, we analyze the finite sample complexity of stochastic system identification
using modern tools from machine learning and statistics. An unknown discrete-time linear …

Statistical learning theory for control: A finite-sample perspective

A Tsiamis, I Ziemann, N Matni… - IEEE Control Systems …, 2023 - ieeexplore.ieee.org
Learning algorithms have become an integral component to modern engineering solutions.
Examples range from self-driving cars and recommender systems to finance and even …

Nonlinear dynamic process monitoring using canonical variate analysis and kernel density estimations

PEP Odiowei, Y Cao - IEEE Transactions on Industrial …, 2009 - ieeexplore.ieee.org
The Principal Component Analysis (PCA) and the Partial Least Squares (PLS) are two
commonly used techniques for process monitoring. Both PCA and PLS assume that the data …

Non-asymptotic identification of linear dynamical systems using multiple trajectories

Y Zheng, N Li - IEEE Control Systems Letters, 2020 - ieeexplore.ieee.org
This letter considers the problem of linear time-invariant (LTI) system identification using
input/output data. Recent work has provided non-asymptotic results on partially observed …

Linear stochastic systems

A Lindquist, G Picci - Series in Contemporary Mathematics, 2015 - Springer
This book is intended to be a treatise on the theory and modeling of secondorder stationary
processes with an exposition of some application areas which we believe are important in …

The role of vector autoregressive modeling in predictor-based subspace identification

A Chiuso - Automatica, 2007 - Elsevier
Subspace identification for closed loop systems has been recently studied by several
authors. A class of new and consistent closed-loop subspace algorithms is based on …

Uncertainty quantification in data-driven stochastic subspace identification

EPB Reynders - Mechanical Systems and Signal Processing, 2021 - Elsevier
A crucial aspect in system identification is the assessment of the accuracy of the identified
system matrices. Stochastic Subspace Identification (SSI) is a widely used approach for the …

Revisiting ho–kalman-based system identification: Robustness and finite-sample analysis

S Oymak, N Ozay - IEEE Transactions on Automatic Control, 2021 - ieeexplore.ieee.org
Weconsider the problem of learning a realization for a linear time-invariant (LTI) dynamical
system from input/output data. Given a single input/output trajectory, we provide finite time …