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NARX Models: Optimal Parametric Approximation of Nonparametric Estimators

Author(s):

G. Ferrari-Trecate, G. De Nicolao
Conference/Journal:

vol. AUT00-25
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.

Year:

2000
Type of Publication:

(04)Technical Report
Supervisor:



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% Autogenerated BibTeX entry
@TechReport { FerNic:2000:IFA_218,
    author={G. Ferrari-Trecate and G. De Nicolao},
    title={{NARX Models: Optimal Parametric Approximation of
	  Nonparametric Estimators}},
    institution={},
    year={2000},
    number={},
    address={},
    month=sep,
    url={http://control.ee.ethz.ch/index.cgi?page=publications;action=details;id=218}
}
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