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 …
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 …
[图书][B] The statistical theory of linear systems
EJ Hannan, M Deistler - 2012 - SIAM
The original edition of this book was published in 1988. The first author, Ted Hannan,
passed away in 1994. Since the book went out of print a decade ago, I have been …
passed away in 1994. Since the book went out of print a decade ago, I have been …
Dynamic textures
Dynamic textures are sequences of images of moving scenes that exhibit certain stationarity
properties in time; these include sea-waves, smoke, foliage, whirlwind etc. We present a …
properties in time; these include sea-waves, smoke, foliage, whirlwind etc. We present a …
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 …
Dynamic textures
Dynamic textures are sequences of images of moving scenes that exhibit certain stationarity
properties in time; these include sea-waves, smoke, foliage, whirlwind but also talking faces …
properties in time; these include sea-waves, smoke, foliage, whirlwind but also talking faces …
A new subspace identification approach based on principal component analysis
Principal component analysis (PCA) has been widely used for monitoring complex industrial
processes with multiple variables and diagnosing process and sensor faults. The objective …
processes with multiple variables and diagnosing process and sensor faults. The objective …
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 …
Uncertainty quantification of the modal assurance criterion in operational modal analysis
Abstract The Modal Assurance Criterion (MAC) is a modal indicator designed to decide
whether the mode shapes used in its computation are corresponding to the same mode …
whether the mode shapes used in its computation are corresponding to the same mode …
Consistent dynamic PCA based on errors-in-variables subspace identification
W Li, SJ Qin - Journal of Process Control, 2001 - Elsevier
In this paper, we make a comparison between dynamic principal component analysis (PCA)
and errors-in-variables (EIV) subspace model identification (SMI) and establish consistency …
and errors-in-variables (EIV) subspace model identification (SMI) and establish consistency …