Identification of Linear Time-invariant, Nonlinear and Time Varying Dynamic Systems Using Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@InProceedings{Yuan:2008:cec,
  author =       "Xiao-Lei Yuan and Yan Bai and Ling Dong",
  title =        "Identification of Linear Time-invariant, Nonlinear and
                 Time Varying Dynamic Systems Using Genetic
                 Programming",
  booktitle =    "2008 IEEE World Congress on Computational
                 Intelligence",
  year =         "2008",
  editor =       "Jun Wang",
  pages =        "56--61",
  address =      "Hong Kong",
  month =        "1-6 " # jun,
  organization = "IEEE Computational Intelligence Society",
  publisher =    "IEEE Press",
  isbn13 =       "978-1-4244-1823-7",
  file =         "EC0029.pdf",
  DOI =          "doi:10.1109/CEC.2008.4630776",
  abstract =     "An improved genetic programming (GP) algorithm was
                 developed in order to use a unified way to identify
                 both linear and nonlinear, both time-invariant and
                 time-varying discrete dynamic systems. 'D' operators
                 and discrete time 'n' terminals were used to construct
                 and evolve difference equations. Crossover operations
                 of the improved GP algorithm were different from the
                 conventional GP algorithm. Two levels of crossover
                 operations were defined. A linear time-invariant
                 system, a nonlinear time-invariant system and a
                 time-varying system were identified by the improved GP
                 algorithm, good models of object systems were achieved
                 with accurate and simultaneous identification of both
                 structures and parameters. GP generated obvious
                 mathematical descriptions (difference equations) of
                 object systems after expression editing, showing
                 correct input-output relationships. It can be seen that
                 GP is good at handling different kinds of dynamic
                 system identification problems and is better than other
                 artificial intelligence (AI) algorithms like neural
                 network or fuzzy logic which only model systems as
                 complete black boxes.",
  keywords =     "genetic algorithms, genetic programming",
  notes =        "WCCI 2008 - A joint meeting of the IEEE, the INNS, the
                 EPS and the IET.",
}

Genetic Programming entries for Xiao-Lei Yuan Yan Bai Ling Dong

Citations