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

Created by W.Langdon from gp-bibliography.bib Revision:1.7630

@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