Results 251 to 260 of about 2,583,502 (287)

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
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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
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Subspace Partitioning for Target Detection and Identification

IEEE Transactions on Signal Processing, 2009
Detection of a given target or set of targets from observed data is a problem countered in many applications. Regardless of the algorithm selected, detection performance can be severely degraded when the subspace defined by the target data set is singular or ill conditioned. High correlations between target components and their linear combinations lead
Wei Wang 0018, Tülay Adali, Darren Emge
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Subspace approximation with applications to system identification

Proceedings of the 2000 American Control Conference. ACC (IEEE Cat. No.00CH36334), 2000
In this paper, a novel approach for parameter identification of linear time invariant (LTI) systems using matrix pencils and ESPRIT-type methods is presented. The relations between Hankel matrices formed from the truncated impulse response and the companion matrix of the poles of the system are fully investigated.
Mohammed A. Hasan, Ali A. Hasan
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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
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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
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Subspace identification of closed-loop systems

Proceedings of the 41st SICE Annual Conference. SICE 2002., 2003
We briefly review a stochastic realization theory based subspace method for identifying closed loop systems. Using the preliminary orthogonal decomposition we show that, under the assumption that the exogenous input is persistently excited, the identification of closed loop systems is divided into two subproblems: the deterministic identification of ...
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Subspace algorithms for the stochastic identification problem

[1991] Proceedings of the 30th IEEE Conference on Decision and Control, 1993
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Peter Van Overschee, Bart De Moor
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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
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Subspace identification of deterministic bilinear systems

Proceedings of the 2000 American Control Conference. ACC (IEEE Cat. No.00CH36334), 2000
In this paper, a 'three block' subspace method for the identification of deterministic bilinear systems is developed. The input signal to the system does not have to be white, which is a major advantage over an existing subspace method for bilinear systems.
Huixin Chen, Jan M. Maciejowski
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