Wiener Model Identification using Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.4420

  author =       "Yueh-Chun Lu and Ming-Hung Chang and Te-Jen Su",
  title =        "Wiener Model Identification using Genetic
  booktitle =    "Proceedings of the International MultiConference of
                 Engineers and Computer Scientists, IMECS 2008",
  year =         "2008",
  volume =       "II",
  address =      "Hong Kong",
  month =        "19-21 " # mar,
  keywords =     "genetic algorithms, genetic programming, Wiener model,
                 system identification, Akaike information criterion
  isbn13 =       "978-988-17012-1-3",
  URL =          "",
  URL =          "",
  size =         "5 pages",
  bibsource =    "OAI-PMH server at",
  contributor =  "CiteSeerX",
  language =     "en",
  oai =          "oai:CiteSeerXPSU:",
  pages =        "1261--1265",
  abstract =     "A Wiener model consists of a dynamic linear transfer
                 function in series with a static nonlinear function. We
                 can through the essences of GP, like robustness, domain
                 independence and ability to search for satisfying
                 solutions in solving complicated nonlinear problems,
                 this study hoped that the evolved GP models could have
                 a better applicability and accuracy of evaluations, and
                 easily obtain the correct structure and parameters of
                 the nonlinear function, and number of zeros and poles
                 of the linear transfer function. GP is applied to the
                 determine nonlinearity and unknown parameters in the
                 nonlinear function and linear dynamic system model are
                 estimated by a least square algorithm. The results of
                 numerical studies indicate the usefulness of proposed
                 approach to Wiener model identification.",

Genetic Programming entries for Yueh-Chun Lu Ming-Hung Chang Te-Jen Su