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 …
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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Elsevier logo Journals & Books Search RegisterSign in View PDF Download full issue Search …
Elsevier logo Journals & Books Search RegisterSign in View PDF Download full issue Search …
Finite sample analysis of stochastic system identification
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 …
using modern tools from machine learning and statistics. An unknown discrete-time linear …
Statistical learning theory for control: A finite-sample perspective
Learning algorithms have become an integral component to modern engineering solutions.
Examples range from self-driving cars and recommender systems to finance and even …
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 …
commonly used techniques for process monitoring. Both PCA and PLS assume that the data …
Non-asymptotic identification of linear dynamical systems using multiple trajectories
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 …
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 …
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 …
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 …
system matrices. Stochastic Subspace Identification (SSI) is a widely used approach for the …
Revisiting ho–kalman-based system identification: Robustness and finite-sample analysis
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 …
system from input/output data. Given a single input/output trajectory, we provide finite time …