Data-based local smoothing technique for parameters estimation of nonlinear ARX models

Elvis J. Alegria, Celso P. Bottura

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2 Citas (Scopus)


This paper proposes a parameter estimation method for a kind of nonlinear auto-regressive (NARX) model, which is usually highly nonlinear because its parameters could vary very fast, since they are unknown nonlinear functions of past observations, called here as mapping-regressors. These parameters are poorly estimated by the standard recursive least-squares (RLS) filter since they vary much faster than standard time-varying parameters (TVP). So, our proposal reduces the fast parameters variability locally by reducing the a priori known mapping-regressors variability. This process is done by using both a reordering process according to the ascendant value of one of the mapping-regressors and the non-temporal windowing intersections of the remaining mapping-regressors. As a result, a set of local smoothed models, where a conventional recursive RLS filter works, is obtained. Experimentally, this approach works faster and simpler than alternative methods from the literature, which are discussed briefly through two simulated examples.

Idioma originalInglés
Título de la publicación alojada2019 American Control Conference, ACC 2019
EditorialInstitute of Electrical and Electronics Engineers Inc.
Número de páginas6
ISBN (versión digital)9781538679265
EstadoPublicada - jul. 2019
Evento2019 American Control Conference, ACC 2019 - Philadelphia, Estados Unidos
Duración: 10 jul. 201912 jul. 2019

Serie de la publicación

NombreProceedings of the American Control Conference
ISSN (versión impresa)0743-1619


Conferencia2019 American Control Conference, ACC 2019
País/TerritorioEstados Unidos


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