Model learning for switching linear systems with autonomous mode transitions

Lars Blackmore, Stephanie Gil, Seung Chung, Brian Williams

Research output: Chapter in Book/Report/Conference proceedingConference contribution

13 Citations (Scopus)

Abstract

We present a novel method for model learning in hybrid discrete-continuous systems. The approach uses approximate Expectation-Maximization to learn the Maximum-Likelihood parameters of a switching linear system. The approach extends previous work by 1) considering autonomous mode transitions, where the discrete transitions are conditioned on the continuous state, and 2) learning the effects of control inputs on the system. We evaluate the approach in simulation.

Original languageEnglish (US)
Title of host publicationProceedings of the 46th IEEE Conference on Decision and Control 2007, CDC
Pages4648-4655
Number of pages8
DOIs
StatePublished - Dec 1 2007
Externally publishedYes
Event46th IEEE Conference on Decision and Control 2007, CDC - New Orleans, LA, United States
Duration: Dec 12 2007Dec 14 2007

Other

Other46th IEEE Conference on Decision and Control 2007, CDC
CountryUnited States
CityNew Orleans, LA
Period12/12/0712/14/07

Fingerprint

Switching Systems
Maximum likelihood
Linear systems
Linear Systems
Expectation Maximization
Continuous System
Discrete Systems
Maximum Likelihood
Evaluate
Model
Simulation
Learning

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Modeling and Simulation
  • Control and Optimization

Cite this

Blackmore, L., Gil, S., Chung, S., & Williams, B. (2007). Model learning for switching linear systems with autonomous mode transitions. In Proceedings of the 46th IEEE Conference on Decision and Control 2007, CDC (pp. 4648-4655). [4434779] https://doi.org/10.1109/CDC.2007.4434779

Model learning for switching linear systems with autonomous mode transitions. / Blackmore, Lars; Gil, Stephanie; Chung, Seung; Williams, Brian.

Proceedings of the 46th IEEE Conference on Decision and Control 2007, CDC. 2007. p. 4648-4655 4434779.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Blackmore, L, Gil, S, Chung, S & Williams, B 2007, Model learning for switching linear systems with autonomous mode transitions. in Proceedings of the 46th IEEE Conference on Decision and Control 2007, CDC., 4434779, pp. 4648-4655, 46th IEEE Conference on Decision and Control 2007, CDC, New Orleans, LA, United States, 12/12/07. https://doi.org/10.1109/CDC.2007.4434779
Blackmore L, Gil S, Chung S, Williams B. Model learning for switching linear systems with autonomous mode transitions. In Proceedings of the 46th IEEE Conference on Decision and Control 2007, CDC. 2007. p. 4648-4655. 4434779 https://doi.org/10.1109/CDC.2007.4434779
Blackmore, Lars ; Gil, Stephanie ; Chung, Seung ; Williams, Brian. / Model learning for switching linear systems with autonomous mode transitions. Proceedings of the 46th IEEE Conference on Decision and Control 2007, CDC. 2007. pp. 4648-4655
@inproceedings{8a92b73477c34441a34a7fa024dfbd3f,
title = "Model learning for switching linear systems with autonomous mode transitions",
abstract = "We present a novel method for model learning in hybrid discrete-continuous systems. The approach uses approximate Expectation-Maximization to learn the Maximum-Likelihood parameters of a switching linear system. The approach extends previous work by 1) considering autonomous mode transitions, where the discrete transitions are conditioned on the continuous state, and 2) learning the effects of control inputs on the system. We evaluate the approach in simulation.",
author = "Lars Blackmore and Stephanie Gil and Seung Chung and Brian Williams",
year = "2007",
month = "12",
day = "1",
doi = "10.1109/CDC.2007.4434779",
language = "English (US)",
isbn = "1424414989",
pages = "4648--4655",
booktitle = "Proceedings of the 46th IEEE Conference on Decision and Control 2007, CDC",

}

TY - GEN

T1 - Model learning for switching linear systems with autonomous mode transitions

AU - Blackmore, Lars

AU - Gil, Stephanie

AU - Chung, Seung

AU - Williams, Brian

PY - 2007/12/1

Y1 - 2007/12/1

N2 - We present a novel method for model learning in hybrid discrete-continuous systems. The approach uses approximate Expectation-Maximization to learn the Maximum-Likelihood parameters of a switching linear system. The approach extends previous work by 1) considering autonomous mode transitions, where the discrete transitions are conditioned on the continuous state, and 2) learning the effects of control inputs on the system. We evaluate the approach in simulation.

AB - We present a novel method for model learning in hybrid discrete-continuous systems. The approach uses approximate Expectation-Maximization to learn the Maximum-Likelihood parameters of a switching linear system. The approach extends previous work by 1) considering autonomous mode transitions, where the discrete transitions are conditioned on the continuous state, and 2) learning the effects of control inputs on the system. We evaluate the approach in simulation.

UR - http://www.scopus.com/inward/record.url?scp=62749157534&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=62749157534&partnerID=8YFLogxK

U2 - 10.1109/CDC.2007.4434779

DO - 10.1109/CDC.2007.4434779

M3 - Conference contribution

AN - SCOPUS:62749157534

SN - 1424414989

SN - 9781424414987

SP - 4648

EP - 4655

BT - Proceedings of the 46th IEEE Conference on Decision and Control 2007, CDC

ER -