• DocumentCode
    1726461
  • Title

    Chaotic system identification via genetic algorithm

  • Author

    Caponetto, R.C. ; Fortuna, L. ; Manganaro, G. ; Xibilia, M.G.

  • Author_Institution
    Catania Univ., Italy
  • fYear
    1995
  • Firstpage
    170
  • Lastpage
    174
  • Abstract
    In this paper a novel approach for identifying the asymptotic behaviour of non-linear chaotic dynamic systems is proposed. The problem has been faced like an optimisation procedure and it has been solved by using the genetic algorithm approach. Given the asymptotic time evolution of the state variables of the non-linear system that has to be identified, they are used to synchronise another system with the same mathematical model. The difference between the given time evolution and the derived trajectories is used in order to define the functional to be minimised
  • Keywords
    chaos; genetic algorithms; identification; nonlinear dynamical systems; asymptotic behaviour; asymptotic time evolution; chaotic dynamic systems; derived trajectories; functional; genetic algorithm; state variables; time evolution;
  • fLanguage
    English
  • Publisher
    iet
  • Conference_Titel
    Genetic Algorithms in Engineering Systems: Innovations and Applications, 1995. GALESIA. First International Conference on (Conf. Publ. No. 414)
  • Conference_Location
    Sheffield
  • Print_ISBN
    0-85296-650-4
  • Type

    conf

  • DOI
    10.1049/cp:19951044
  • Filename
    501667