Stochastic nonlinear system identification using multi-objective multi-population parallel genetic programming

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

@InProceedings{Yuan:2009:CCDC,
  author =       "Xiao-lei Yuan and Yan Bai",
  title =        "Stochastic nonlinear system identification using
                 multi-objective multi-population parallel genetic
                 programming",
  booktitle =    "Chinese Control and Decision Conference, CCDC '09",
  year =         "2009",
  month =        jun,
  pages =        "1148--1153",
  keywords =     "genetic algorithms, genetic programming,
                 multiobjective fitness definition, multiobjective
                 multipopulation parallel genetic programming, nonlinear
                 autoregressive with exogenous inputs polynomial models,
                 object systems, stochastic nonlinear system
                 identification, nonlinear systems, stochastic systems",
  DOI =          "doi:10.1109/CCDC.2009.5192053",
  abstract =     "To realize simultaneous identification of both
                 structures and parameters of stochastic nonlinear
                 systems, multi-population parallel genetic programming
                 (GP) was employed. Object systems were represented by
                 nonlinear autoregressive with exogenous inputs (NARX)
                 and nonlinear autoregressive moving average with
                 exogenous inputs (NARMAX) polynomial models,
                 multi-objective fitness definition was used to restrict
                 sizes of individuals during the evolution. For all
                 examples, multi-population parallel GP found accurate
                 models for object systems, simultaneously identified
                 structures and parameters. In comparison with
                 traditional single-population GP, multi-population GP
                 showed a more competitive performance in avoiding
                 premature convergence, and was much more efficient in
                 searching for good models for object systems. From
                 identification results, it can be concluded that
                 multi-population parallel GP is good at handling
                 complex stochastic nonlinear system identification
                 problems and is superior to other existing
                 identification methods.",
  notes =        "Also known as \cite{5192053}",
}

Genetic Programming entries for Xiao-Lei Yuan Yan Bai

Citations