Subspace methods for system identification

By 片山徹. 2005

484 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/84970b6a37395abdae357d7ed3c1999cd521186e

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Citations in the corpus

Optimal instrumental variable method for closed-loop identification (2011, background): “[9] for a survey) or transfer function model form.”
An Online Spectral Learning Algorithm for Partially Observable Nonlinear Dynamical Systems (2011, background): “In this respect, TPSRs are closely related to the transformed representations of LDSs and HMMs found by subspace identification (Van Overschee and De Moor 1996; Katayama 2005; Soatto and Chiuso 2001; Hsu, Kakade, and Zhang 2009).”
Closed-loop and activity-guided optogenetic control. (2015, unknown): “Once estimated, these models offer computational advantages in the form of a smaller state space to consider, along with better stability and generalization to new data (Katayama, 2006).”
Data-Driven Model Predictive Control With Stability and Robustness Guarantees (2019, methodology): “Although this result has found various applications in the field of system identification [13]–[15], it has only recently been used to develop data-driven methods for system analysis and control with theoretical guarantees.”
An overview of subspace identification (2006, methodology): “IMs offer an attractive alternative to input-output methods due o simple and general parametrization for MIMO systems (there s no linear input-output parametrization that is general enough or all linear MIMO systems, see (Katayama, 2005)).”
A Regularized Linear Dynamical System Framework for Multivariate Time Series Analysis (2015, methodology): “This can be done using standard LDS learning approaches such as the Expectation-Maximization (EM) (Ghahramani and Hinton 1996) or spectral learning (Katayama 2005; Van Overschee and De Moor 1996) algorithms.”
Bootstrapping bilinear models of Simple Vehicles (2015, background): “There is a complete theory for linear systems (Van Overschee and De Moor, 1996; Katayama, 1999) as well as for some limited classes of nonlinear systems, such as linear systems followed by a static nonlinearity (Giri and Bai, 2010) and Volterra series (Franz and Schölkopf, 2006).”
From model-based control to data-driven control: Survey, classification and perspective (2013, background): “In literatures, the subspace approach [66,77,98], the data space approach [38,69,103] and the data-driven simulation approach [89–91] have been shown to share the idea that system dynamics are represented as a subspace of a finite-dimensional vector space, which consists of the time series data of i”