A Bayesian approach to identification of hybrid systems
By Aleksandar Lj. Juloski, Siep Weiland, W. M. Heemels. IEEE Transactions on Automatic Control, 2004
321 citations recorded by the source corpus.
Subfields: Control and Systems Engineering, Management Science and Operations Research
View Paper: https://www.semanticscholar.org/paper/ec971e8afcec8a99e53f2ec9cf2db38b7f70bc59
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Control and Systems Engineering
- Francesco Borrelli
- Manfred Morari
- Alberto Bemporad
- Aleksandar Lj. Juloski
- Simone Paoletti
- Giancarlo Ferrari-Trecate
- René Vidal
- Matthew J. Johnson
In Management Science and Operations Research
- Francesco Borrelli
- Manfred Morari
- Matthew J. Johnson
- René Vidal
- Andrew C. Miller
- Ryan P. Adams
- Alberto Bemporad
- David M. Blei
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.”
- Bayesian Learning and Inference in Recurrent Switching Linear Dynamical Systems (2017, result): “This work is similar in spirit to the piecewise affine (PWA) framework in control systems [Sontag, 1981, Juloski et al., 2005, Paoletti et al., 2007].”
- 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].”