System Identification Using Genetic Programming and Gene Expression Programming

  • Juan J. Flores
  • Mario Graff
Conference paper
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.


Genetic Programming Genetic Operator Gene Expression Programming High Order System High Order Equation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Ljung, L.S.: System Identification: Theory for the User. Prentice-Hall, Englewood Cliffs (1987)zbMATHGoogle 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)zbMATHCrossRefGoogle 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 

Personalised recommendations