Summary

Abstract

All methods elaborated in the book combine the nonparametric and parametric tools. Such a strategy allows to solve various kinds of specific obstacles, which are difficult to be overcome in purely parametric or purely nonparametric approach. In particular, the global identification problem can be decomposed on simpler local problems, the measurement sequence can be pre-filtered in the nonparametric stage, or the rough parametric model can be refined by the nonparametric correction when the number of measurements is large enough. The schemes proposed in the book can be used elastically and have a lot of degrees of freedom. In most of them we can obtain traditional parametric or nonparametric procedures by simple avoiding of the selected steps of combined algorithms. In this sense, the proposed ideas can be treated as generalizations of classical approaches to system identification. Below, to summarize the contribution, in Section 10.1 we itemize the most significant problems solved in the book, and next, in Section 10.2 we discuss selected problems remained for further studies.