A bayesian framework for large-scale identification of nonlinear hybrid systems

Ahmad Madary; Hamid Reza Momeni; Alessandro Abate; Kim G. Larsen

doi:10.1016/j.ifacol.2021.08.508

A bayesian framework for large-scale identification of nonlinear hybrid systems

Ahmad Madary, Hamid Reza Momeni, Alessandro Abate, Kim G. Larsen

Research output: Contribution to journal › Conference article in Journal › Research › peer-review

3 Citations (Scopus)

33 Downloads (Pure)

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 language	English
Book series	IFAC-PapersOnLine
Volume	54
Issue number	5
Pages (from-to)	259-264
Number of pages	6
ISSN	2405-8963
DOIs	https://doi.org/10.1016/j.ifacol.2021.08.508
Publication status	Published - 1 Jul 2021
Event	7th IFAC Conference on Analysis and Design of Hybrid Systems, ADHS 2021 - Brussels, Belgium Duration: 7 Jul 2021 → 9 Jul 2021

Conference

Conference	7th IFAC Conference on Analysis and Design of Hybrid Systems, ADHS 2021
Country/Territory	Belgium
City	Brussels
Period	07/07/2021 → 09/07/2021
Sponsor	IFAC TC 1.4 Stochastic Systems, IFAC TC 1.5 Networked Systems, IFAC TC 2.1 Control Design, IFAC TC 5.1. Manufacturing Plant Control, IFAC TC 6.4 Fault Detection, Supervision and Safety of Techn.Processes - SAFEPROCESS, International Federation of Automatic Control (IFAC) - Technical Committee on Discrete Event and Hybrid Systems, TC 1.3.

Bibliographical note

Publisher Copyright:
Copyright © 2021 The Authors.

Keywords

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

Access to Document

10.1016/j.ifacol.2021.08.508

1-s2.0-S2405896321012830-mainFinal published version, 432 KBLicence: CC BY-NC-ND 4.0

AUB Link

Search for the material in Aalborg University Library's search engine

Cite this

@inproceedings{b596d7cdc0774f86adc4c88ac4e37e2e,

title = "A bayesian framework for large-scale identification of nonlinear hybrid systems",

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.",

keywords = "Bayesian inference, Large data sets, Nonlinear hybrid systems, Occam's razor principle, Switched nonlinear arx models, System identification",

author = "Ahmad Madary and Momeni, {Hamid Reza} and Alessandro Abate and Larsen, {Kim G.}",

note = "Publisher Copyright: Copyright {\textcopyright} 2021 The Authors.; 7th IFAC Conference on Analysis and Design of Hybrid Systems, ADHS 2021 ; Conference date: 07-07-2021 Through 09-07-2021",

year = "2021",

month = jul,

day = "1",

doi = "10.1016/j.ifacol.2021.08.508",

language = "English",

volume = "54",

pages = "259--264",

journal = "IFAC-PapersOnLine",

issn = "2405-8963",

publisher = "Elsevier",

number = "5",

}

TY - GEN

T1 - A bayesian framework for large-scale identification of nonlinear hybrid systems

AU - Madary, Ahmad

AU - Momeni, Hamid Reza

AU - Abate, Alessandro

AU - Larsen, Kim G.

PY - 2021/7/1

Y1 - 2021/7/1

N2 - 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.

AB - 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.

KW - Bayesian inference

KW - Large data sets

KW - Nonlinear hybrid systems

KW - Occam's razor principle

KW - Switched nonlinear arx models

KW - System identification

UR - http://www.scopus.com/inward/record.url?scp=85119014925&partnerID=8YFLogxK

U2 - 10.1016/j.ifacol.2021.08.508

DO - 10.1016/j.ifacol.2021.08.508

M3 - Conference article in Journal

AN - SCOPUS:85119014925

SN - 2405-8963

VL - 54

SP - 259

EP - 264

JO - IFAC-PapersOnLine

JF - IFAC-PapersOnLine

IS - 5

T2 - 7th IFAC Conference on Analysis and Design of Hybrid Systems, ADHS 2021

Y2 - 7 July 2021 through 9 July 2021

ER -

A bayesian framework for large-scale identification of nonlinear hybrid systems

Abstract

Conference

Bibliographical note

Keywords

Access to Document

AUB Link

Other files and links

Fingerprint

Cite this