Genetic programming-based modeling on chaotic time series

Created by W.Langdon from gp-bibliography.bib Revision:1.4215

  author =       "Wei Zhang and Gen-Ke Yang and Zhi-Ming Wu",
  title =        "Genetic programming-based modeling on chaotic time
  booktitle =    "Proceedings of the third International Conference on
                 Machine Learning and Cybernetics (ICMLC 2004)",
  year =         "2004",
  volume =       "4",
  pages =        "2347--2352",
  address =      "Shanghai",
  month =        "26-29 " # aug,
  publisher =    "IEEE Press",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "",
  DOI =          "doi:10.1109/ICMLC.2004.1382192",
  size =         "6 pages",
  abstract =     "One of the difficulties in nonlinear time series
                 analysis is how to reconstruct the system model from
                 the data series. This is mainly due to the dissipation
                 and 'butterfly' effect of the chaotic systems. This
                 paper proposes a genetic programming-based modeling
                 (GPM) algorithm for the chaotic time series. In GPM,
                 genetic programming-based techniques are used to search
                 for appropriate model structures in the function space,
                 and the particle swarm optimization (PSO) algorithm is
                 introduced for nonlinear parameter estimation (NPE) on
                 dynamic model structures. In addition, the results of
                 nonlinear time series analysis (NTSA) are integrated
                 into the GPM to improve the modeling quality and the
                 criterion of the established models. The effectiveness
                 of such improvements is proved by modeling the
                 experiments on known chaotic time series.",
  notes =        "Dept. of Autom., Shanghai Jiao Tong Univ., China",

Genetic Programming entries for Wei Zhang Gen-Ke Yang Zhi-Ming Wu