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

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

*Corresponding author af dette arbejde

Publikation: Bidrag til tidsskrift/Konferencebidrag i tidsskrift /Bidrag til avisKonferenceartikelForskningpeer review

3 Citationer (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.

OriginalsprogEngelsk
BogserieIFAC-PapersOnLine
Vol/bind54
Nummer5
Sider (fra-til)259-264
Antal sider6
ISSN2405-8963
DOI
StatusUdgivet - jul. 2021
Udgivet eksterntJa
Begivenhed7th IFAC Conference on Analysis and Design of Hybrid Systems - UClouvain, BRUSSELS, Belgien
Varighed: 7 jul. 20219 jul. 2021
https://sites.uclouvain.be/adhs21/

Konference

Konference7th IFAC Conference on Analysis and Design of Hybrid Systems
LokationUClouvain
Land/OmrådeBelgien
ByBRUSSELS
Periode07/07/202109/07/2021
Internetadresse

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