Identification of linear systems with hard input nonlinearities of known structure
type: Item
instance of: scholarly article
title: Identification of linear systems with hard input nonlinearities of known structure
zbMATH Open document ID: 1010.93032
DOI: 10.1016/S0005-1098(01)00281-3
published in: Automatica
publication date: 2002-09-05T00:00:00Z
review text: This paper studies identification of linear systems with input nonlinearities of known structure. For input nonlinearities parametrized by one parameter, a deterministic approach is proposed based on the idea of separable least squares. The identification problem becomes equivalent to a one-dimensional minimization problem. The method may be used for several common static and nonstatic input nonlinearities. For a general input nonlinearity, a correlation analysis based identification algorithm is presented which is shown to be convergent. Two examples are studied.
reviewed by: Yves Cherruault
Mathematics Subject Classification ID: 93B30
Mathematics Subject Classification ID: 93C10
zbMATH DE Number: 1797062
zbMATH Keywords: system identification
zbMATH Keywords: parameter estimation
zbMATH Keywords: nonlinear systems
zbMATH Keywords: Hammerstein systems
zbMATH Keywords: input nonlinearities
zbMATH Keywords: separable least squares
zbMATH Keywords: correlation analysis
author: Er-wei Bai
MaRDI profile type: Publication
cites work: An optimal two-stage identification algorithm for Hammerstein-Wiener nonlinear systems
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cites work: Parameter identification of discontinuous Hammerstein systems
description: scientific article; zbMATH DE number 1797062