Stochastic nonlinear system identification based on HFC-GP

Created by W.Langdon from gp-bibliography.bib Revision:1.7615

@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