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 |
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Book series | IFAC-PapersOnLine |
Volume | 54 |
Issue | 5 |
Pages (from-to) | 259-264 |
Number of pages | 6 |
ISSN | 2405-8963 |
DOIs | |
Publication status | Published - Jul 2021 |
Externally published | Yes |
Event | 7th IFAC Conference on Analysis and Design of Hybrid Systems - UClouvain, BRUSSELS, Belgium Duration: 7 Jul 2021 → 9 Jul 2021 https://sites.uclouvain.be/adhs21/ |
Conference
Conference | 7th IFAC Conference on Analysis and Design of Hybrid Systems |
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Location | UClouvain |
Country/Territory | Belgium |
City | BRUSSELS |
Period | 07/07/2021 → 09/07/2021 |
Internet address |
Keywords
- Bayesian inference
- Large data sets
- Nonlinear hybrid systems
- Occam's razor principle
- Switched nonlinear arx models
- System identification