Nonlinear modelling and identification

Author

Patra, Amit ; Unbehauen, Heinz

Author_Institution

Autom. Control Lab., Ruhr-Univ., Bochum, Germany

fYear

1993

fDate

17-20 Oct 1993

Firstpage

441

Abstract

There has been a considerable increase in activity in the field of identification of nonlinear systems. Side by side with the identification of discrete-time models based on Kolmogorov-Gabor polynomials, artificial neural networks, etc., there has been a great deal of progress in the identification of continuous-time models governed by ordinary differential equations. This paper attempts to give an overview of the existing modelling frameworks and makes a comparison among them on the basis of their approximating abilities, computational requirements, on-line applicability etc

Keywords

continuous time systems; identification; neural nets; nonlinear systems; Kolmogorov-Gabor polynomials; approximating abilities; computational requirements; continuous-time models; discrete-time models; identification; nonlinear modelling; nonlinear systems; online applicability; ordinary differential equations; Adaptive control; Artificial neural networks; Automatic control; Laboratories; Linear systems; Nonlinear systems; Parameter estimation; Phase estimation; Programmable control; Stability;

fLanguage

English

Publisher

ieee

Conference_Titel

Systems, Man and Cybernetics, 1993. 'Systems Engineering in the Service of Humans', Conference Proceedings., International Conference on

Conference_Location

Le Touquet

Print_ISBN

0-7803-0911-1

Type

conf

DOI

10.1109/ICSMC.1993.385051

Filename

385051