Nuclear Norm Subspace System Identification and Its Application on a Stochastic Model of Plague

The discrete-time model of plague is deduced by zero-order holder based on the continuous-time model. Due to the existence of stochastic disturbances, the stochastic model is given corresponding to the discrete-time model. The state estimation and noise reduction of the stochastic model are achieved...

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Bibliographic Details
Published in:Journal of systems science and complexity 2020-02, Vol.33 (1), p.43-60
Main Authors: Yu, Miao, Liu, Jianchang, Zhao, Lichun
Format: Article
Language:English
Subjects:
Approximation
Complex Systems
Computer simulation
Continuous time systems
Control
Kalman filters
Mathematical analysis
Mathematics
Mathematics and Statistics
Mathematics of Computing
Noise intensity
Noise reduction
Operations Research/Decision Theory
Partial differential equations
Singular value decomposition
State estimation
Statistics
Stochastic models
System identification
Systems Theory
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Summary:The discrete-time model of plague is deduced by zero-order holder based on the continuous-time model. Due to the existence of stochastic disturbances, the stochastic model is given corresponding to the discrete-time model. The state estimation and noise reduction of the stochastic model are achieved by designing Kalman filter. Nuclear norm minimization is to structure the low-rank matrix approximation instead of the singular value decomposition in the process of subspace system identification. According to the plague data from the World Health Organization, the system matrices and noise intensity of the model are identified. Simulations are carried out to show the higher approximation capability of the proposed method.
ISSN:1009-6124
1559-7067
DOI:10.1007/s11424-019-8003-9