@inproceedings{e4a7e714903e4dfb8ed8dba3759e495e,
title = "Active learning for linear parameter-varying system identification",
abstract = "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.",
keywords = "Machine learning, System identification, Parameter estimation, Uncertainty, Diesel engines",
author = "Robert Chin and Maass, {Alejandro I.} and Nalika Ulapane and Chris Manzie and Iman Shames and Dragan Nesic and Rowe, {Jonathon E.} and Hayato Nakada",
year = "2020",
month = may,
day = "8",
language = "English",
series = "IFAC-PapersOnLine",
publisher = "Elsevier",
booktitle = "21st IFAC World Congress (IFAC-V 2020)",
note = "21st IFAC World Congress (IFAC-V 2020) ; Conference date: 12-07-2020 Through 17-07-2020",
}