Piecewise regression represents a powerful tool to derive accurate yet modular models describing complex phenomena or physical systems. This paper presents an approach for learning PieceWise NonLinear (PWNL) functions in both a supervised and semi-supervised setting. We further equip the proposed technique with a method for the automatic generation of additional unsupervised data, which are leveraged to improve the overall accuracy of the estimate. The performance of the proposed approach is preliminarily assessed on two simple simulation examples, where we show the benefits of using nonlinear local models and artificially generated unsupervised data.

(2021). Piecewise nonlinear regression with data augmentation . Retrieved from http://hdl.handle.net/10446/193750

Piecewise nonlinear regression with data augmentation

Mazzoleni, Mirko;
2021-01-01

Abstract

Piecewise regression represents a powerful tool to derive accurate yet modular models describing complex phenomena or physical systems. This paper presents an approach for learning PieceWise NonLinear (PWNL) functions in both a supervised and semi-supervised setting. We further equip the proposed technique with a method for the automatic generation of additional unsupervised data, which are leveraged to improve the overall accuracy of the estimate. The performance of the proposed approach is preliminarily assessed on two simple simulation examples, where we show the benefits of using nonlinear local models and artificially generated unsupervised data.
2021
Mazzoleni, Mirko; Breschi, Valentina; Formentin, Simone
File allegato/i alla scheda:
File Dimensione del file Formato  
2021 IFAC SYSID - PWNL paper.pdf

accesso aperto

Versione: publisher's version - versione editoriale
Licenza: Creative commons
Dimensione del file 547.52 kB
Formato Adobe PDF
547.52 kB Adobe PDF Visualizza/Apri
Pubblicazioni consigliate

Aisberg ©2008 Servizi bibliotecari, Università degli studi di Bergamo | Terms of use/Condizioni di utilizzo

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10446/193750
Citazioni
  • Scopus 4
  • ???jsp.display-item.citation.isi??? 4
social impact