Results 131 to 140 of about 9,311 (164)

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
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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 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
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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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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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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 ...
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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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Identification of stable models in subspace identification by using regularization

IEEE Transactions on Automatic Control, 2001
The authors consider the identification problem for linear combined deterministic-stochastic systems for which special cases, both deterministic and stochastic, use a subspace identification method with regularization. It is well known that in common subspace identification methods, the system matrices are estimated by the least squares method, but for
Tony Van Gestel, Johan A. K. Suykens, Paul Van Dooren, Bart De Moor   +3 more
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Uncertainties Quantification for Subspace Identification of Rotating Systems

IFAC Proceedings Volumes, 2013
Abstract The dynamics of rotating systems such as helicopters and wind turbines show periodically time-varying behavior. Differently from the linear time-invariant case, such systems cannot be characterized by the classical modal parametrization. An alternative description is made possible with the Floquet theory which extends the modal analysis and ...
Jhinaoui, Ahmed, Mevel, Laurent, Morlier, Joseph   +2 more
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