System Identification Using Genetic Programming and Gene Expression Programming

  • Juan J. Flores
  • Mario Graff
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3733)


This paper describes a computer program called ECSID that automates the process of system identification using Genetic Programming and Gene Expression Programming. ECSID uses a function set, and the observed data to determine an ODE whose behavior is similar to the observed data. ECSID is capable to evolve linear and non-linear models of higher order systems. ECSID can also code a higher order system as a set of higher order equations. ECSID has been tested with linear pendulum, non-linear pendulum, mass-spring system, linear circuit, etc.


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  1. 1.
    Ljung, L.S.: System Identification: Theory for the User. Prentice-Hall, Englewood Cliffs (1987)MATHGoogle Scholar
  2. 2.
    Koza, J.R.: Genetic Programming: On the Programming of Computers by Means of Natural Selection (Complex Adaptive Systems). The MIT Press, Cambridge (1992)Google Scholar
  3. 3.
    Ferreira, C.: Gene expression programming: A new adaptive algorithm for solving problems. Complex Systems 2, 87–129 (2001)Google Scholar
  4. 4.
    Bradley, E., Stolle, R.: Automatic construction of accurate models of physical systems. Technical report (University of Colorado, Department of Computer Science)Google Scholar
  5. 5.
    Gray, G.J., Murray-Smith, D.J., Li, Y., Sharman, K.C.: Nonlinear model structure identification using genetic programming. In: Koza, J.R. (ed.) Late Breaking Papers at the Genetic Programming 1996 Conference Stanford University, Stanford University, CA, USA, July 28-31, pp. 32–37. Stanford Bookstore (1996)Google Scholar
  6. 6.
    Weinbrenner, T.: Genetic programming techniques applied to measurement data. Diploma Thesis (1997)Google Scholar
  7. 7.
    Cao, H., Kang, L., Chen, Y., Yu, J.: Evolutionary modeling of systems of ordinary differential equations with genetic programming. Genetic Programming and Evolvable Machines 1, 309–337 (2000)MATHCrossRefGoogle Scholar
  8. 8.
    Hinchliffe, M.: Dynamic Modelling Using Genetic Programming. PhD thesis, University of Newcastle upon Type (2001)Google Scholar
  9. 9.
    Graff, M., Flores, J.J.: (2005),

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Juan J. Flores
    • 1
  • Mario Graff
    • 1
  1. 1.División de Estudios de Posgrado, Facultad de Ingeniería EléctricaUniversidad Michoacana de San Nicolas de Hidalgo 

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