Pub. online:5 Aug 2022Type:Research ArticleOpen Access
Journal:Informatica
Volume 16, Issue 1 (2005), pp. 131–144
Abstract
The aim of the given paper is the development of an approach for parametric identification of Wiener systems with piecewise linear nonlinearities, i.e., when the linear part with unknown parameters is followed by a saturation-like function with unknown slopes. It is shown here that by a simple data reordering and by a following data partition the problem of identification of a nonlinear Wiener system could be reduced to a linear parametric estimation problem. Afterwards, estimates of the unknown parameters of linear regression models are calculated by processing respective particles of input-output data. A technique based on ordinary least squares (LS) is proposed here for the estimation of parameters of linear and nonlinear parts of the Wiener system, including the unknown threshold of piecewise nonlinearity, too. The results of numerical simulation and identification obtained by processing observations of input-output signals of a discrete-time Wiener system with a piecewise nonlinearity by computer are given.
Journal:Informatica
Volume 24, Issue 1 (2013), pp. 35–58
Abstract
The aim of the given paper is development of an approach based on reordering of observations to be processed for the extraction of an unmeasurable internal intermediate signal, that acts between linear dynamical and static nonlinear blocks of the Wiener system with hard-nonlinearity of the known structure. The technique based on the ordinary least squares (LS) and on data partition is used for the internal signal extraction. The results of numerical simulation and identification of a discrete-time Wiener system with five types of hard-nonlinearities, such as saturation, dead-zone, preload, backlash, and, discontinuous nonlinearity are given by computer.