Piecewise affine (PWA) regression is a supervised learning method which aims at estimating, from a set of training data, a PWA map approximating the relationship between a set of explanatory variables (commonly called regressors) and continuous-valued outputs. In this paper, we describe a recursive and numerically efficient PWA regression algorithm, and discuss its application to the identification of multi-input multi-output PWA dynamical models in autoregressive form and to the identification of linear parameter-varying models.

Identification of hybrid and linear parameter varying models via recursive piecewise affine regression and discrimination

Breschi V.;
2017

Abstract

Piecewise affine (PWA) regression is a supervised learning method which aims at estimating, from a set of training data, a PWA map approximating the relationship between a set of explanatory variables (commonly called regressors) and continuous-valued outputs. In this paper, we describe a recursive and numerically efficient PWA regression algorithm, and discuss its application to the identification of multi-input multi-output PWA dynamical models in autoregressive form and to the identification of linear parameter-varying models.
2016 European Control Conference, ECC 2016
978-1-5090-2591-6
File in questo prodotto:
Non ci sono file associati a questo prodotto.

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11311/1167009
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 10
  • ???jsp.display-item.citation.isi??? 6
social impact