NARX Models: Optimal Parametric Approximation of Nonparametric Estimators

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/sup 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/sup 2/) focusing on their use in the parametric identification of NARX models.