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• W2895596731 abstract "Abstract A method for nuclear norm-based recursive subspace prediction of time-varying continuous-time stochastic systems via distribution theory is proposed. The random distribution theory is adopted to describe the time-derivative of stochastic processes, which is the key to obtain the input–output algebraic equation. The low-rank matrix approximation of the input–output projection matrix is established by nuclear norm minimization instead of the singular value decomposition. Moreover, the optimization problem is deduced by the alternating direction method of multipliers. According to the angle rotation between past and present subspaces spanned by the extended observability matrices, the future signal subspace is predicted by the present subspace. Further, the system matrices are predicted and the corresponding system model is obtained. The results of simulation studies show the effectiveness of the presented method." @default.
• W2895596731 created "2018-10-12" @default.
• W2895596731 creator A5055490135 @default.
• W2895596731 creator A5057442821 @default.
• W2895596731 date "2018-11-01" @default.
• W2895596731 modified "2023-10-17" @default.
• W2895596731 title "Nuclear norm-based recursive subspace prediction of time-varying continuous-time stochastic systems via distribution theory" @default.
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• W2895596731 doi "https://doi.org/10.1016/j.jfranklin.2018.09.020" @default.
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