Nuclear Norm Subspace System Identification and Its Application on a Stochastic Model of Plague |
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Authors: | Yu Miao Liu Jianchang Zhao Lichun |
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Institution: | 1.College of Information Science and Engineering, Northeastern University, Shenyang, 110819, China ;2.State Key Laboratory of Synthetical Automation for Process Industries, Northeastern University, Shenyang, 110819, China ;3.School of Control Engineering, Northeastern University at Qinhuangdao, Qinhuangdao, 066004, China ;4.School of Mathematics and Information Science, Anshan Normal University, Anshan, 114007, China ; |
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Abstract: | The discrete-time model of plague is deduced by zero-order holder based on the continuoustime 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. |
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