A Bayesian Framework for Large-Scale Identification of Nonlinear Hybrid Systems

Ahmad Madary*, Hamid Reza Momeni*, Alessandro Abate*, Kim Guldstrand Larsen*

*Corresponding author for this work

Research output: Contribution to journal/Conference contribution in journal/Contribution to newspaperConference articleResearchpeer-review

3 Citations (Scopus)

Abstract

In this paper, a two-level Bayesian framework is proposed for the identification of nonlinear hybrid systems from large data sets by embedding it. in a four-stage procedure. At the first stage, feature vector selection techniques are used to generate a reduced-size set from the given training data set. The resulting data set then is used to identify the hybrid system using a Bayesian method, where the objective is to assign each data point to a corresponding sub-mode of the hybrid model. At the third stage, this data assignment is used to train a Bayesian classifier to separate the original data set. and determine the corresponding sub-mode for all the original data points. Finally, once every data point is assigned to a sub-mode, a Bayesian estimator is used to estimate a regressor for each sub-system independently. The proposed method tested on three case studies.

Original languageEnglish
Book seriesIFAC-PapersOnLine
Volume54
Issue5
Pages (from-to)259-264
Number of pages6
ISSN2405-8963
DOIs
Publication statusPublished - Jul 2021
Externally publishedYes
Event7th IFAC Conference on Analysis and Design of Hybrid Systems - UClouvain, BRUSSELS, Belgium
Duration: 7 Jul 20219 Jul 2021
https://sites.uclouvain.be/adhs21/

Conference

Conference7th IFAC Conference on Analysis and Design of Hybrid Systems
LocationUClouvain
Country/TerritoryBelgium
CityBRUSSELS
Period07/07/202109/07/2021
Internet address

Keywords

  • Bayesian inference
  • Large data sets
  • Nonlinear hybrid systems
  • Occam's razor principle
  • Switched nonlinear arx models
  • System identification

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