An algebraic geometric approach to the identification of a class of linear hybrid systems

By René Vidal, Stefano Soatto, Yi Ma, S. Sastry. 42nd IEEE International Conference on Decision and Control (IEEE Cat. No.03CH37475), 2003

344 citations recorded by the source corpus.

Subfields: Control and Systems Engineering

View Paper: https://www.semanticscholar.org/paper/e718369996bfe27796859a9f8cdaa3f78912bf5e

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In Control and Systems Engineering

Citations in the corpus

Predictive Control for Linear and Hybrid Systems (2017, background): “Identification techniques for piecewise affine systems were recently developed [104, 255, 163, 39, 165, 282, 227], that allow one to derive models (or parts of models) from input/output data.”
Identification of Hybrid Systems: A Tutorial (2007, methodology): “In this section, four procedures for the identification of SARX/PWARX models are briefly discussed, namely the algebraic procedure [51, 74, 78], the clustering-based procedure [27], the Bayesian procedure [47], and the bounded-error procedure [5].”
Low rank subspace clustering (LRSC) (2014, methodology): “…vision, e.g., image segmentation (Yang et al., 2008), motion segmentation (Vidal et al., 2008) and face clustering (Ho et al., 2003), image processing, e.g., image representation and compression (Hong et al., 2006), and systems theory, e.g., hybrid system identification (Vidal et al., 2003b).”
A Geometric Analysis of Subspace Clustering with Outliers (2011, background): “Other applications of subspace clustering in computer vision include image segmentation [48], face clustering [18], image representation and compression [19], and systems theory [44].”
A Bayesian approach to identification of hybrid systems (2004, methodology): “This is an advantage over the region estimation procedures presented in [8] and [ 9 ].”
Data-based hybrid modelling of the component placement process in pick-and-place machines (2004, methodology): “Moreover, PWARX models provide an input–output description for a fairly large class of discrete-time PWA systems without logic states (Vidal et al., 2003).”
Bayesian Nonparametric Inference of Switching Dynamic Linear Models (2010, methodology): “The Dirichlet process has proven useful in many applications due to its clustering properties, which are clearly seen by examining thepredictive distributionof draws θ′i ∼ G0.”
A bounded-error approach to piecewise affine system identification (2005, background): “In [10] the authors propose an algebraic geometric solution to the identification of piecewise linear (PWL) models which establishes a connection between PWL system identification, polynomial factorization, and hyperplane clustering.”