User choices for nonparametric preprocessing in system identification

Abstract Most research on system identification is focused on the identification of parametric models, for example a transfer function or a state space model where the information is condensed in a few parameters. In the daily practice, nonparametric methods, like frequency response function measurements, are intensively used. Recently, it was indicated that nonparametric identification methods could be used to robustify the parametric identification framework. A nonparametric preprocessing step can also be used to reduce or even eliminate the required user interaction, making system identification accessible for a much wider user group. For that reason, there is an increasing interest in nonparametric identification. In order to choose, compare, and to benchmark these nonparametric methods, it is very important to select the proper criteria. In this paper we identify and discuss the important choices that should be considered. It will be shown that these strongly depend on the intended use of the nonparametric model..