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Subspace identification for a stochastic model of bubonic plague
2016 35th Chinese Control Conference (CCC), 2016Based on the model of bubonic plague, the corresponding stochastic model with stochastic disturbances is given. According to the data of bubonic plague from the World Health Organization, the coefficient matrices and noise intensity of the model are obtained by subspace identification method.
Miao Yu +4 more
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Machine learning-based stochastic subspace identification method for structural modal parameters
Engineering structures, 2023Dawei Liu, Y. Bao, Hui Li
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Operational Modal Analysis Using a Fast Stochastic Subspace Identification Method
Stochastic subspace identification methods are an efficient tool for system identification of mechanical systems in Operational Modal Analysis, where modal parameters (natural frequencies, damping ratios, mode shapes) are estimated from measured ambient vibration data of a structure.Döhler, Michael +2 more
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Stochastic subspace identification of linear systems with observation outliers
21st Mediterranean Conference on Control and Automation, 2013We propose a diagnostic for the state space model fitting time series formed by deleting observations from the data and measuring the change in the estimates of the parameters. A method is proposed for distinguishing an observational outlier from an innovational one.
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Bridge Maintenance, Safety, Management, Life-Cycle Sustainability and Innovations, 2021
K. Kvåle, O. Øiseth
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K. Kvåle, O. Øiseth
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A Note on LQ Decomposition in Stochastic Subspace Identification
2010In this paper, we consider the role of LQ decomposition in the realization-based subspace identification method for discrete-time stochastic systems as a continuation of our earlier work [6] in deterministic setting. Under the assumption that the past horizon of the data matrix is infinite, we reveal a nice block lower triangular structure of a certain
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Uncertainty bounds on modal parameters obtained stochastic subspace identification
2008The modal parameters of a structure that are estimated from ambient vibration measurements are always subject to bias and variance errors. In this paper, it is discussed how part of the bias errors can be removed and how the variance errors can be estimated from a single ambient vibration test.
Reynders, E. +2 more
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