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.
Originalsprog | Engelsk |
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Bogserie | IFAC-PapersOnLine |
Vol/bind | 54 |
Nummer | 5 |
Sider (fra-til) | 259-264 |
Antal sider | 6 |
ISSN | 2405-8963 |
DOI | |
Status | Udgivet - jul. 2021 |
Udgivet eksternt | Ja |
Begivenhed | 7th IFAC Conference on Analysis and Design of Hybrid Systems - UClouvain, BRUSSELS, Belgien Varighed: 7 jul. 2021 → 9 jul. 2021 https://sites.uclouvain.be/adhs21/ |
Konference
Konference | 7th IFAC Conference on Analysis and Design of Hybrid Systems |
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Lokation | UClouvain |
Land/Område | Belgien |
By | BRUSSELS |
Periode | 07/07/2021 → 09/07/2021 |
Internetadresse |