Stochastic nonlinear system identification based on HFC-GP

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@InProceedings{Yuan:2010:CCC,
  author =       "Xiao-Lei Yuan and Yan Bai and Gang Peng and 
                 Zhi-Cun Gao and Peng Li and Rui Ma",
  title =        "Stochastic nonlinear system identification based on
                 HFC-GP",
  booktitle =    "29th Chinese Control Conference (CCC 2010)",
  year =         "2010",
  month =        "29-31 " # jul,
  pages =        "1217--1223",
  URL =          "http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=5573417",
  abstract =     "To identify structures and parameters of complex
                 stochastic nonlinear systems with accuracy and
                 efficiency, preventing premature convergence during the
                 evolution, an improved multi-objective hierarchical
                 fair competition (HFC) parallel genetic programming
                 (GP) algorithm was employed. The improved HFC GP
                 algorithm was used to identify an object system based
                 on nonlinear autoregressive moving average with
                 exogenous inputs (NARMAX)model, good identification
                 results were achieved with simultaneous identification
                 of both structures and parameters of the object system.
                 In comparison with single population GP and traditional
                 multi-population GP, HFC-GP showed a more competitive
                 performance in preventing premature convergence. It can
                 be concluded that HFC-GP is good at solving complex
                 stochastic nonlinear system identification problems and
                 is superior to other existing identification methods.",
  keywords =     "genetic algorithms, genetic programming, HFC-GP
                 algorithm, NARMAX model, complex stochastic nonlinear
                 system identification, multiobjective hierarchical fair
                 competition, nonlinear autoregressive moving average
                 with exogenous input, object system, parallel genetic
                 programming, autoregressive moving average processes,
                 identification, large-scale systems, nonlinear control
                 systems, parallel algorithms, stochastic systems",
  notes =        "In chinese. Also known as \cite{5573417}",
}

Genetic Programming entries for Xiao-Lei Yuan Yan Bai Gang Peng Zhi-Cun Gao Peng Li Rui Ma

Citations