2018
DOI: 10.1016/j.isatra.2018.08.014
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Nuclear norm subspace identification for continuous-time stochastic systems based on distribution theory method

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Cited by 11 publications
(4 citation statements)
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“…It represents the logical step in complexity between the linear systems which are inadequate in the modeling of various dynamic processes and the difficult realm of nonlinear systems [24]. Therefore, to well capture the nonlinear dynamics of complex ironmaking process as well as consider the simplicity and easy-using of the model, the bilinear system model will be developed for the MIQ indices by subspace identification (SI) method, which is computationally efficient and can be applied to modeling of MIMO systems directly [5], [25], [26]. Different from the standard SI algorithms, such as CVA and N4SID [26], [27], this paper will establish an input-output bilinear model only by constructing the extended Hankel matrices to facilitate the calculation of the subspace matrices without explicitly computing the system matrices.Thus the method proposed in this paper can further improve the computational efficiency and ease to realize online learning.…”
Section: Online Learning Modeling and Data-driven Predictive Contmentioning
confidence: 99%
“…It represents the logical step in complexity between the linear systems which are inadequate in the modeling of various dynamic processes and the difficult realm of nonlinear systems [24]. Therefore, to well capture the nonlinear dynamics of complex ironmaking process as well as consider the simplicity and easy-using of the model, the bilinear system model will be developed for the MIQ indices by subspace identification (SI) method, which is computationally efficient and can be applied to modeling of MIMO systems directly [5], [25], [26]. Different from the standard SI algorithms, such as CVA and N4SID [26], [27], this paper will establish an input-output bilinear model only by constructing the extended Hankel matrices to facilitate the calculation of the subspace matrices without explicitly computing the system matrices.Thus the method proposed in this paper can further improve the computational efficiency and ease to realize online learning.…”
Section: Online Learning Modeling and Data-driven Predictive Contmentioning
confidence: 99%
“…More knowledge about identification of stochastic subspace can be seen is in Reference [40]. More knowledge about identification of closed-loop subspace can be seen is in Reference [41] or pages 55-78 of Reference [37].…”
Section: Conflicts Of Interestmentioning
confidence: 99%
“…As a way to overcome the drawback of requiring physicalbased models, the process models obtained from subspace identification (SI) methods have gained attention in manufacturing systems management, since these methods directly deliver a state-space model of less complex implementation for the design of model-based control strategies [12]. As a consequence, SI models have had significant application to the modeling of complex, large-scale, and time-varying-parameter systems [13,14,15]. Then, due to the applicability of SI for obtaining models to be used in model-based control design (in particular for model predictive control -MPC -design), they have been widely used in manufacturing industries [16,17].…”
Section: Introductionmentioning
confidence: 99%