Can I read Active Learning for Linear Parameter-Varying System Identification on EtoBox?

Active Learning for Linear Parameter-Varying System Identification by Chin, Robert; Maass, Alejandro I.; Ulapane, Nalika; Manzie, Chris; Shames, Iman; Nešić, Dragan; Rowe, Jonathan E.; Nakada, Hayato is a scholarly article available to read on EtoBox.

What is Active Learning for Linear Parameter-Varying System Identification about?

Active learning is proposed for selection of the next operating points in the design of experiments, for identifying linear parameter-varying systems. We extend existing approaches found in literature to multiple-input multiple-output systems with a multivariate scheduling parameter. Our approach is based on exploiting the probabilistic features of Gaussian process regression to quantify the overall model uncertainty across locally identified models. This results in a flexible framework which accommodates for various techniques to be applied for estimation of local linear models and their corresponding uncertainty. We perform active learning in application to the identification of a diesel engine air-path model, and demonstrate that measures of model uncertainty can be successfully reduced using the proposed framework.

Author

Chin, Robert; Maass, Alejandro I.; Ulapane, Nalika; Manzie, Chris; Shames, Iman; Nešić, Dragan; Rowe, Jonathan E.; Nakada, Hayato

Published

2020

Language

EN