Results 271 to 280 of about 13,903 (307)

Subspace identification by orthogonal decomposition

IFAC Proceedings Volumes, 1999
Abstract There is experimental evidence that the N4SID method performs poorly in certain conditions where the past signals (past inputs and past outputs) and future input spaces are nearly parallel. This paper describes a subspace identification technique based on a preliminary orthogonal decomposition of the data spaces which is more robust and ...
CHIUSO, ALESSANDRO, PICCI, GIORGIO
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An approach to recursive subspace identification

2017 IEEE 56th Annual Conference on Decision and Control (CDC), 2017
In this paper, an approach to recursive subspace identification based on the coordinate-free framework of subspace identification is proposed. Herein, the predictor space serves as a natural basis for the formulation of a recursive approach. Compressing the necessary past information, the predictor space of a previous identification will be used for ...
Andreas Bathelt, Dirk Söffker, Mohieddine Jelali   +2 more
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Subspace identification with eigenvalue constraints

Automatica, 2013
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Daniel N. Miller, Raymond A. de Callafon
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Subspace identification of circulant systems

Automatica, 2008
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Paolo Massioni, Michel Verhaegen
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On Consistency of Subspace Methods for System Identification

Automatica, 1998
The authors consider consistency of subspace methods for system identification. They give conditions ensuring consistency of the subspace methods used in subspace identification methods. For systems without noise, a persistence of excitation condition on the input signal can ensure the consistency. Moreover, the authors give a system for which subspace
Magnus Jansson, Bo Wahlberg
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On the ill-conditioning of subspace identification with inputs

Automatica, 2004
The authors present an error analysis which applies to some commonly used subspace identification methods with inputs. They show that a presence of collinearity of the regressors these methods may lead to inaccurate estimates of the system parameters.
CHIUSO, ALESSANDRO, PICCI, GIORGIO
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Imposing stability in subspace identification by regularization

Proceedings of the 39th IEEE Conference on Decision and Control (Cat. No.00CH37187), 2002
In subspace methods for linear system identification, the system matrices are usually estimated by least squares, based on estimated Kalman filter state sequences and the observed inputs and outputs. For an infinite number of data points and a correct choice of the system order, this least squares estimate of the system matrices is unbiased.
Tony Van Gestel, Johan A. K. Suykens, Paul Van Dooren, Bart De Moor   +3 more
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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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Subspace identification with moment matching

Automatica, 2019
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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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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