New methods for the identification of nonlinear model structures based upon genetic programming techniques

• Autorzy: Winkler S. , Affenzeller M. , Wagner S.
• Języki publikacji: EN

Abstrakty

EN

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.

Słowa kluczowe

EN

genetic algorithm; genetic programming; mechatronic system; nonlinear model identification; time series analysis

PL

algorytm genetyczny

• Wydawca: Oficyna Wydawnicza Politechniki Wrocławskiej
• Czasopismo: Systems Science
• Rocznik: 2005
• Tom: Vol. 31, no 1
• Strony: 5--13
• Opis fizyczny: Bibliogr. 12 poz., rys., wykr.

Twórcy

autor

Winkler S.

• Institute of Systems Theory and Simulation, Johannes Kepler University Linz, Austria

autor

Affenzeller M.

• Institute of Systems Theory and Simulation, Johannes Kepler University Linz, Austria

autor

Wagner S.

• Institute of Systems Theory and Simulation, Johannes Kepler University Linz, Austria

Bibliografia

• [1] Affenzeller M., Wagner S., SASEGASA: A New Generic Parallel Evolutionary Algorithm for Achieving Highest Quality Results, Journal of Heuristics, Special Issue on New Advances on Parallel Meta-Heuristics for Complex Problems, Kluwer Academic Publishers, Vol. 10, 2004, pp. 239-263.
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• [3] Gray G. et al.. Nonlinear Model Structure Identification Using Genetic Programming, Control Engineering Practice 6, 1998.
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• [5] Koza J., Genetic Programming: On the Programming of Computers by means of Natural Selection, The MIT Press, Cambridge, Mass., 1992.
• [6] Koza J., Genetic Programming for Econometric Modeling, Intelligent Systems for Finance and Business, 1995, 251-269.
• [7] Langdon W., Poli R., Foundations of Genetic Programming, Springer Verlag, Berlin, Heidelberg, New York, 2002.
• [8] Langley p. et al.. Scientific Discovery: Computational Explorations of the Creative Process, The MIT Press, Cambridge, Mass., 1987.
• [9] Michalewicz Z., Genetic Algorithms + Data Structures = Evolution Programs (3rd ed.), Springer-Verlag, Berlin, Heidelberg, New York, 1996.
• [10]schwefel H., Numerische Optimierung von Computer-Modellen mittels Evolutionsstrategie, Birkhauser Verlag, Basel, 1994.
• [11] Wagner S., Affenzeller M., HeuristicLab - A Generic and Extensible Optimization Environment, to be published in: Proceedings of IBERAMIA 2004.
• [12] Winkler S. et al.. Identifying Nonlinear Model Structures Using Genetic Programming Techniques, Cybernetics and Systems, 2004, 689-694.