Particle filtering based identification for autonomous nonlinear ODE models**This work was supported by the project Probabilistic modeling of dynamical systems (Contract number: 621-2013-5524) funded by the Swedish Research Council. (*) are members of the LCCC Linnaeus Center and the ELLIIT Excellence Center at Lund University

Ode Models ◽

Nonlinear Ode

Sequential Monte Carlo Methods for System Identification**This work was supported by the projects Learning of complex dynamical systems (Contract number: 637-2014-466) and Probabilistic modeling of dynamical systems (Contract number: 621-2013-5524), both funded by the Swedish Research Council.

IFAC-PapersOnLine ◽

10.1016/j.ifacol.2015.12.224 ◽

2015 ◽

Vol 48 (28) ◽

pp. 775-786 ◽

Cited By ~ 9

Author(s):

Thomas B. Schön ◽

Fredrik Lindsten ◽

Johan Dahlin ◽

Johan Wågberg ◽

Christian A. Naesseth ◽

...

Keyword(s):

Monte Carlo ◽

System Identification ◽

Dynamical Systems ◽

Monte Carlo Methods ◽

Research Council ◽

Sequential Monte Carlo ◽

Probabilistic Modeling ◽

Complex Dynamical Systems ◽

Sequential Monte Carlo Methods ◽

Swedish Research Council

On Identification via EM with Latent Disturbances and Lagrangian Relaxation**This work was supported by the Australian Research Council (DP130100551), and the Swedish Research Council (VR) as part of the project: Probabilistic modeling of dynamical systems (Contract number: 621-2013-5524).

IFAC-PapersOnLine ◽

10.1016/j.ifacol.2015.12.102 ◽

2015 ◽

Vol 48 (28) ◽

pp. 69-74

Author(s):

Jack Umenberger ◽

Johan Wågberg ◽

Ian R. Manchester ◽

Thomas B. Schön

Keyword(s):

Dynamical Systems ◽

Lagrangian Relaxation ◽

Research Council ◽

Probabilistic Modeling ◽

Australian Research Council ◽

Swedish Research Council

Online sparse Gaussian process regression using FITC and PITC approximations**This research is supported by the Dutch Technology Foundation STW, which is part of the Netherlands Organisation for Scientific Research (NWO), and which is partly funded by the Ministry of Economic Affairs. The work was also supported by the Swedish research Council (VR) via the project Probabilistic modeling of dynamical systems (Contract number: 621-2013-5524).

IFAC-PapersOnLine ◽

10.1016/j.ifacol.2015.12.212 ◽

2015 ◽

Vol 48 (28) ◽

pp. 703-708 ◽

Cited By ~ 8

Author(s):

Hildo Bijl ◽

Jan-Willem van Wingerden ◽

Thomas B. Schön ◽

Michel Verhaegen

Keyword(s):

Dynamical Systems ◽

The Netherlands ◽

Gaussian Process ◽

Research Council ◽

Scientific Research ◽

Gaussian Process Regression ◽

Probabilistic Modeling ◽

Technology Foundation

Bayesian nonparametric identification of piecewise affine ARX systems**This work was supported by the projects: Learning of complex dy-namical systems (Contract number: 637-2014-466) and Probabilistic modeling of dynamical systems (Contract number: 621-2013-5524), both funded by the Swedish Research Council.

IFAC-PapersOnLine ◽

10.1016/j.ifacol.2015.12.213 ◽

2015 ◽

Vol 48 (28) ◽

pp. 709-714 ◽

Author(s):

Johan Wågberg ◽

Fredrik Lindsten ◽

Thomas B. Schön

Keyword(s):

Dynamical Systems ◽

Research Council ◽

Probabilistic Modeling ◽

Nonparametric Identification ◽

Bayesian Nonparametric ◽

Piecewise Affine ◽

Swedish Research Council

Nonlinear State Space Smoothing Using the Conditional Particle Filter**This work was supported by the project Probabilistic modelling of dynamical systems (Contract number: 621-2013-5524) and CADICS, a Linnaeus Center, both funded by the Swedish Research Council (VR).

IFAC-PapersOnLine ◽

10.1016/j.ifacol.2015.12.257 ◽

2015 ◽

Vol 48 (28) ◽

pp. 975-980 ◽

Author(s):

Andreas Svensson ◽

Thomas B. Schön ◽

Manon Kok

Keyword(s):

Dynamical Systems ◽

Particle Filter ◽

State Space ◽

Research Council ◽

Probabilistic Modelling ◽

Nonlinear State

Quasi-Newton particle Metropolis-Hastings**E-mail address to corresponding author: [email protected]. This work was supported by: Learning of complex dynamical systems (Contract num. 637-2014-466), Probabilistic modelling of dynamical systems (Contract num. 621-2013-5524) and CADICS, a Linnaeus Center, all funded by the Swedish Research Council.

IFAC-PapersOnLine ◽

10.1016/j.ifacol.2015.12.258 ◽

2015 ◽

Vol 48 (28) ◽

pp. 981-986 ◽

Author(s):

Johan Dahlin ◽

Fredrik Lindsten ◽

Thomas B. Schön

Keyword(s):

Dynamical Systems ◽

Research Council ◽

Probabilistic Modelling ◽

Complex Dynamical Systems ◽

Mail Address ◽

Quasi Newton ◽

E Mail

Pseudo-Marginal MCMC for Parameter Estimation in α-Stable Distributions**The first author acknowledges partial funding from EPSRC-DTG- 2013/EU, while the second and third authors were supported by the EPSRC BTaRoT project EP/K020153/1; the third author was also funded by the Learning of complex dynamical systems project (Contract number: 637-2014-466) from the Swedish Research Council.

IFAC-PapersOnLine ◽

10.1016/j.ifacol.2015.12.173 ◽

2015 ◽

Vol 48 (28) ◽

pp. 472-477 ◽

Cited By ~ 2

Author(s):

Marina Riabiz ◽

Fredrik Lindsten ◽

Simon Godsill

Keyword(s):

Parameter Estimation ◽

Dynamical Systems ◽

Research Council ◽

Stable Distributions ◽

Complex Dynamical Systems ◽

The Third ◽

Project Contract

On kernel structures for regularized system identification (I): a machine learning perspective**This work has been supported by a research grant for junior researchers No. 621-2014-5894 and the Linnaeus Center CADICS, both funded by the Swedish Research Council, and the ERC advanced grant LEARN, No. 267381, funded by the European Research Council.http://www.hamecmopsys.ens2m.fr.

IFAC-PapersOnLine ◽

10.1016/j.ifacol.2015.12.267 ◽

2015 ◽

Vol 48 (28) ◽

pp. 1035-1040 ◽

Cited By ~ 5

Author(s):

Tianshi Chen ◽

Lennart Ljung

Keyword(s):

Machine Learning ◽

System Identification ◽

Research Council ◽

European Research ◽

Research Grant

An Asynchronous Algorithm for Optimal Vehicle Coordination at Traffic Intersections * *This work was supported by Copplar (project number 32226302), the Swedish Research Council (VR, grant number 2012-4038) and the European Commission Seventh Framework (AdaptIVe, grant number 610428)

IFAC-PapersOnLine ◽

10.1016/j.ifacol.2017.08.2124 ◽

2017 ◽

Vol 50 (1) ◽

pp. 12008-12014

Author(s):

Mario Zanon ◽

Sébastien Gros ◽

Henk Wymeersch ◽

Paolo Falcone

Keyword(s):

European Commission ◽

Research Council ◽

Asynchronous Algorithm ◽

Traffic Intersections

Analysis and Mitigation of Bias Injection Attacks Against a Kalman Filter * *This work was supported by the Swedish Civil Contingencies Agency through the CERCES project, the Swedish Research Council, Knut and Alice Wallenberg Foundation, and the Swedish Foundation for Strategic Research

IFAC-PapersOnLine ◽

10.1016/j.ifacol.2017.08.1564 ◽

2017 ◽

Vol 50 (1) ◽

pp. 8393-8398 ◽

Author(s):

Jezdimir Miloševič ◽

Takashi Tanaka ◽

Henrik Sandberg ◽

Karl Henrik Johansson

Keyword(s):

Kalman Filter ◽

Research Council ◽

Strategic Research ◽

Injection Attacks