A Multi-objective Genetic Programming/ NARMAX Approach to Chaotic Systems Identification

Created by W.Langdon from gp-bibliography.bib Revision:1.4524

@InProceedings{Han:2006:WCICA,
  author =       "Pu Han and Shiliang Zhou and Dongfeng Wang",
  title =        "A Multi-objective Genetic Programming/ NARMAX Approach
                 to Chaotic Systems Identification",
  booktitle =    "The Sixth World Congress on Intelligent Control and
                 Automation, WCICA 2006",
  year =         "2006",
  volume =       "1",
  pages =        "1735--1739",
  address =      "Dalian",
  publisher =    "IEEE",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "1-4244-0332-4",
  DOI =          "doi:10.1109/WCICA.2006.1712650",
  abstract =     "A chaotic system identification approach based on
                 genetic programming (GP) and multi-objective
                 optimisation is introduced. NARMAX (Nonlinear Auto
                 Regressive Moving Average with exogenous inputs) model
                 representation is used for the basis of the
                 hierarchical tree encoding in GP. Criteria related to
                 the complexity, performance and chaotic invariants
                 obtained by chaotic time series analysis of the models
                 are considered in the fitness evaluation, which is
                 achieved using the concept of the non-dominated
                 solutions. So the solution set provides a trade-off
                 between the complexity and the performance of the
                 models, and derived model were able to capture the
                 dynamic characteristics of the system and reproduce the
                 chaotic motion. The simulation results show that the
                 proposed technique provides an efficient method to get
                 the optimum NARMAX difference equation model of chaotic
                 systems",
  notes =        "Dept. of Autom., North China Electr. Power Univ.,
                 Baoding",
}

Genetic Programming entries for Pu Han Shi-Liang Zhou Dongfeng Wang

Citations