Bayesian regression, a nonparametric identification technique with several appealing features, can be applied to the identification of NARX (nonlinear ARX) models. However, its computational complexity scales as O(N^3) where N is the data set size. In order to reduce complexity, the challenge is to obtain fixed-order parametric models capable of approximating accurately the nonparametric Bayes estimate avoiding its explicit computation. In this work we derive, optimal finite-dimensional approximations of complexity O(N^2) focusing on their use in the parametric identification of NARX models.
NARX Models: Optimal Parametric Approximation of Nonparametric Estimators
FERRARI TRECATE, GIANCARLO;DE NICOLAO, GIUSEPPE
2001-01-01
Abstract
Bayesian regression, a nonparametric identification technique with several appealing features, can be applied to the identification of NARX (nonlinear ARX) models. However, its computational complexity scales as O(N^3) where N is the data set size. In order to reduce complexity, the challenge is to obtain fixed-order parametric models capable of approximating accurately the nonparametric Bayes estimate avoiding its explicit computation. In this work we derive, optimal finite-dimensional approximations of complexity O(N^2) focusing on their use in the parametric identification of NARX models.File in questo prodotto:
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