期刊
SYSTEMS & CONTROL LETTERS
卷 83, 期 -, 页码 28-37出版社
ELSEVIER SCIENCE BV
DOI: 10.1016/j.sysconle.2015.06.008
关键词
Bilinear models; Wiener-Hammerstein models; LNL systems; Fixed point; Iterative algorithm; Contraction mapping; Kernel machine; Nonlinear systems identification
Inspired by fixed point theory, an iterative algorithm is proposed to identify bilinear models recursively in this paper. It is shown that the resulting iteration is a contraction mapping on a metric space when the number of input-output data points approaches infinity. This ensures the existence and uniqueness of a fixed point of the iterated function sequence and therefore the convergence of the iteration. As an application, one class of block-oriented systems represented by a cascade of a dynamic linear (L), a static nonlinear (N) and a dynamic linear (L) subsystems is illustrated. This gives a solution to the long-standing convergence problem of iteratively identifying LNL (Winer-Hammerstein) models. In addition, we extend the static nonlinear function (N) to a nonparametric model represented by using kernel machine. (C) 2015 Elsevier B.V. All rights reserved.
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