Results 281 to 290 of about 13,903 (307)

Fault detection: a subspace identification approach

Proceedings of the 40th IEEE Conference on Decision and Control (Cat. No.01CH37228), 2002
Some results on the analysis of the fault detection problem in a subspace identification framework are presented and two approaches are proposed, exploiting an existing perturbation analysis of subspace methods.
LOVERA, MARCO, PARISINI, THOMAS, M. Verhaegen   +2 more
openaire   +3 more sources

Subspace identification with constraints on the impulse response

International Journal of Control, 2016
ABSTRACTSubspace identification methods may produce unreliable model estimates when a small number of noisy measurements are available. In such cases, the accuracy of the estimated parameters can be improved by using prior knowledge about the system. The prior knowledge considered in this paper is constraints on the impulse response. It is motivated by
Ivan Markovsky, Guillaume Mercère
openaire   +2 more sources

Subspace algorithms for the stochastic identification problem

[1991] Proceedings of the 30th IEEE Conference on Decision and Control, 1993
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Peter Van Overschee, Bart De Moor
openaire   +1 more source

Subspace identification of distributed, decomposable systems

Proceedings of the 48h IEEE Conference on Decision and Control (CDC) held jointly with 2009 28th Chinese Control Conference, 2009
This article concerns the identification of a class of linear systems which we call “decomposable systems”. Such systems can be thought of as the interconnection of a number of identical subsystems, and they can be used to model a number of large scale systems.
Paolo Massioni, Michel Verhaegen
openaire   +1 more source

On Weighting of Data Matrix in Subspace Identification

2007 46th IEEE Conference on Decision and Control, 2007
The MOESP types of the subspace algorithms which are originally proposed by Verhaegen are considered at the point of view from the weighting of the data matrices. We have proposed an interpretation of these types of subspace algorithms by using the Schur complement (SC) of the data product moment and derive a unified framework for the subspace-based ...
Yoshinori Takei, Hidehito Nanto, Shunshoku Kanae, Zi-Jiang Yang, Kiyoshi Wada   +4 more
openaire   +1 more source

Subspace identification methods and fMRI analysis

2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2008
The main goal of this paper is to propose application of modern multidimensional systems identification algorithms of the subspace identification theory in the context of fMRI data analysis. The methods originated in 1990s in the field of process control and identification and yield robust linear model parameter estimates for systems with many inputs ...
Jana, Tauchmanova, Martin, Hromcik
openaire   +2 more sources

Subspace identification of piecewise linear systems

2004 43rd IEEE Conference on Decision and Control (CDC) (IEEE Cat. No.04CH37601), 2004
Subspace identification can be used to obtain models of piecewise linear state-space systems for which the switching is known. The models should not switch faster than the block size of the Hankel matrices used. The nonconsecutive parts of the input and output data that correspond to one of the local linear systems can be used to obtain the system ...
Vincent Verdult, Michel Verhaegen
openaire   +1 more source

Fast Identification of Koopman-Invariant Subspaces: Parallel Symmetric Subspace Decomposition

2020 American Control Conference (ACC), 2020
This paper presents a parallel data-driven method to identify finite-dimensional subspaces that are invariant under the Koopman operator describing a dynamical system. Our approach builds on Symmetric Subspace Decomposition (SSD), which is a centralized scheme to find Koopman-invariant subspaces and Koopman eigenfunctions.
Masih Haseli, Jorge Cortés 0001
openaire   +1 more source

Tensor regression for LTI subspace identification

2015 American Control Conference (ACC), 2015
The biggest bottleneck of Linear Parameter Varying (LPV) subspace identification methods is the unavoidable over-parametrization in its first, rank-revealing estimation step. This motivated us to look at less superfluous parametrizations for Linear Time Invariant (LTI) subspace methods which have the potential to be extended to the LPV case.
Bilal Gunes, Jan-Willem van Wingerden, Michel Verhaegen   +2 more
openaire   +1 more source

Statistically robust signal subspace identification

International Conference on Acoustics, Speech, and Signal Processing, 2002
The problem of signal subspace identification in the presence of transient, high-power noise or non-Gaussian noise is considered. To overcome such problems, an algorithm that results in a statistically robust singular value decomposition is proposed. This algorithm is derived from the connection between least-squares regression and the singular value ...
openaire   +1 more source