New Methods for the Identification of Nonlinear Model Structures Based Upon Genetic Programming Techniques

Publikation: Beitrag in Buch/Bericht/TagungsbandKonferenzbeitragBegutachtung

15 Zitate (Scopus)

Abstract

Identifying nonlinear model structures as a part of analyzing a physical system means trying to generate an algebraic expression as a part of an equation that describes the physical representation of a dynamic system. Many existing system identification methods are based on parameter identification. In this paper we describe a method using genetic programming to evolve an algebraic representation of measured input-output response data. The main advantage of the presented approach is that unlike many other identification methods, it does not restrict the set of models that can be identified but can be applied to any kind of data sets representing a system's observed or simulated input and output signals. This paper describes research that was done for the project "Specification, Design and Implementation of a Genetic Programming Approach for Identifying Nonlinear Models of Mechatronic Systems". The goal of the project is to find models for mechatronic systems; our task was to examine, whether the methods of Genetic Programming are suitable for determining the structures of physical systems by analyzing a system's measured behaviour or not.

OriginalspracheEnglisch
TitelProceedings of the 15th International Conference on Systems Science
Herausgeber (Verlag)Wroclaw University of Technology Press
Seiten386-393
Seitenumfang8
PublikationsstatusVeröffentlicht - 2004
Veranstaltung15th International Conference on Systems Science - Wroclaw, Polen
Dauer: 7 Sep 200410 Sep 2004

Konferenz

Konferenz15th International Conference on Systems Science
LandPolen
OrtWroclaw
Zeitraum07.09.200410.09.2004

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