Results 241 to 250 of about 2,583,502 (287)

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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A framework for subspace identification methods

Proceedings of the 2001 American Control Conference. (Cat. No.01CH37148), 2001
Similarities and differences among various subspace identification methods (MOESP, N4SID and CVA) are examined by putting them in a general regression framework. Subspace identification methods consist of three steps: estimating the predictable subspace for multiple future steps, then extracting state variables from this subspace and finally fitting ...
Ruijie Shi, John F. MacGregor
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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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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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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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Subspace identification with moment matching

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