Título Localised Kinky Inference
Autores Blaas, A. , MANZANO CRESPO, JOSÉ MARÍA, Limon, D. , Calliess, J. , IEEE
Publicación externa No
Medio 2019 18th European Control Conference, Ecc 2019
Alcance Proceedings Paper
Naturaleza Científica
Fecha de publicacion 01/01/2019
ISI 000490488301002
DOI 10.23919/ecc.2019.8796283
Abstract Their flexibility to learn general function classes renders nonparametric regression algorithms particularly attractive in system identification and data-based control settings, where little a priori knowledge about a dynamical system is to be presumed. Building on approaches known as NSM- or Lipschitz regression, we propose a new nonparametic machine learning approach. While it inherits theoretical learning guarantees from the methods it is built upon, it is designed to limit the computational effort both for learning and for generating predictions. This renders our method applicable to online system identification and control settings where the desired sample frequency precludes previous nonparametric approaches from being deployed. Apart from deriving a guarantee on the ability of our method to learn any continuous function, we illustrate some of its practical merits on a number of benchmark comparison problems.
Miembros de la Universidad Loyola

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