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

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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.

Original languageEnglish
Title of host publicationProceedings of the 15th International Conference on Systems Science
PublisherWroclaw University of Technology Press
Pages386-393
Number of pages8
Publication statusPublished - 2004
Event15th International Conference on Systems Science - Wroclaw, Poland
Duration: 7 Sep 200410 Sep 2004

Conference

Conference15th International Conference on Systems Science
CountryPoland
CityWroclaw
Period07.09.200410.09.2004

Keywords

  • Genetic algorithms
  • Genetic programming
  • System identification
  • Time series analysis

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