User friendly Box-Jenkins identification using nonparametric noise models

The identification of SISO linear dynamic systems in the presence of output noise disturbances is considered. A `nonparametric' Box-Jenkins approach is studied: the parametric noise model is replaced by a nonparametric model that is obtained in a preprocessing step, and this without any user interaction. The major advantage for the user is that i) one method can be used to replace the classical ARX, ARMAX, OE, and Box-Jenkins models; ii) no noise model order should be selected. This makes the identification much easier to use for a wider public; iii) a bias on the plant model does not create a bias on the noise model. The disadvantage of the proposed nonparametric approach is a small loss in efficiency with respect to the optimal parametric choice. These results are illustrated on a series of well selected problems.