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2019•International Journal of Modelling Identification and ControlRequires access

Identification of nonlinear systems having discontinuous nonlinearity

Adil Brouri, Laila Kadi, Mohamed Benyassi

Open publisher page 7 citations

Abstract

This paper deals with the identification of nonlinear systems. Presently, the nonlinear system is described by the Wiener model. The linear element is non-parametric and may be of unknown structure. The nonlinear part is allowed to be discontinuous or of hard type and non-invertible. A two-stage identification method is developed to get a set of points of the nonlinear part and estimates of the linear dynamic element. This method is based on simple geometric analysis and involves easily generated excitation signals.

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What this paper is about

This paper deals with the identification of nonlinear systems. Presently, the nonlinear system is described by the Wiener model. The linear element is non-parametric and may be of unknown structure. The nonlinear part is allowed to be discontinuous or of hard type and non-invertible. A two-stage identification method is developed to get a set of points of the nonlinear part and estimates of the linear dynamic element. This method is based on simple geometric analysis and involves easily generated excitation signals.

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OpenAlex reports 7 citations for this work. Citation counts describe recorded attention and do not establish research quality.

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Available abstract

This paper deals with the identification of nonlinear systems. Presently, the nonlinear system is described by the Wiener model. The linear element is non-parametric and may be of unknown structure. The nonlinear part is allowed to be discontinuous or of hard type and non-invertible. A two-stage identification method is developed to get a set of points of the nonlinear part and estimates of the linear dynamic element. This method is based on simple geometric analysis and involves easily generated excitation signals.

Key concepts: Nonlinear system, Nonlinear element, Identification (biology), Invertible matrix, Parametric statistics, Nonlinear system identification, Simple (philosophy), Set (abstract data type)

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