Results 131 to 140 of about 3,141,474 (179)

Identification of stochastic systems : Subspace methods and covariance extension

, 2001
NR ...
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Robust probabilistic canonical correlations for stochastic subspace identification

, 2022
O'Connell, B.J., Cross, E.J., Rogers, T.J.   +2 more
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Uncertainty quantification in data-driven stochastic subspace identification [PDF]

Mechanical Systems and Signal Processing, 2021
Abstract A crucial aspect in system identification is the assessment of the accuracy of the identified system matrices. Stochastic Subspace Identification (SSI) is a widely used approach for the identification of linear systems from output-only data because it combines a high computational robustness and efficiency with a high estimation accuracy ...
Edwin Reynders
exaly   +3 more sources

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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
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Stochastic subspace identification via "LQ decomposition"

42nd IEEE International Conference on Decision and Control (IEEE Cat. No.03CH37475), 2004
A new stochastic subspace identification algorithm is developed with the help of a stochastic realization on a finite interval. First, a finite-interval realization algorithm is re-derived via "block-LDL decomposition" for a finite string of complete covariance sequence.
Hideyuki Tanaka, Tohru Katayama
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Stochastic realization with exogenous inputs and ‘subspace-methods’ identification

Signal Processing, 1996
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
PICCI, GIORGIO, KATAYAMA T.
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Polynomial extension of linear subspace algorithms for stochastic identification

2004 43rd IEEE Conference on Decision and Control (CDC) (IEEE Cat. No.04CH37601), 2004
Among the algorithms of linear models identification from input/output data, the N4SID (numerical sub-space state space system identification) plays an important role due to its simplicity and effectiveness. It is known that N4SDD gives good results for system identification in a Gaussian setting.
Di Loreto C, GERMANI, Alfredo, MANES, COSTANZO   +2 more
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Subspace Identification of Pure Stochastic Systems

2006 IEEE International Conference on Automation, Quality and Testing, Robotics, 2006
In this paper we treat the subspace identification of pure stochastic systems with no external input. The stochastic identification problem consists of computing the stochastic system matrices from given output data only. We show how this can be done using geometric operations as orthogonal projections.
D. Sendrescu, D. Popescu, E. Bobasu, E. Petre   +3 more
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Subspace identification for a stochastic model of plague

International Journal of Biomathematics, 2016
In this paper, a stochastic model of plague is first studied by subspace identification. First, the discrete model of plague is obtained based on the classical model. The corresponding stochastic model is proposed for the existence of stochastic disturbances. Second, for the model, the parameter matrices and noise intensity are obtained.
Yu, Miao, Liu, Jianchang
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An experimental validation of the Stochastic Subspace Identification

PAMM, 2004
AbstractIn this contribution we derive and experimentally validate the Stochastic Subspace Identification. Additionally we compare the results with an updated finite element model. (© 2004 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)
A. S. Kompalka, S. Reese
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