G. Ferrari-Trecate and G. De Nicolao. NARX models: Optimal parametric approximation of nonparametric estimators. Proc. American Control Conference , pages 4868--4873, 2001. Arlington, Virginia, US, 25-27 June.
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.