Identification of Linear Time-invariant, Nonlinear and Time Varying Dynamic Systems Using Genetic Programming

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

@InProceedings{Yuan:2008:cec,

• author = "Xiao-Lei Yuan and Yan Bai and Ling Dong",
• title = "Identification of Linear Time-invariant, Nonlinear and Time Varying Dynamic Systems Using Genetic Programming",
• booktitle = "2008 IEEE World Congress on Computational Intelligence",
• year = "2008",
• editor = "Jun Wang",
• pages = "56--61",
• address = "Hong Kong",
• month = "1-6 " # jun,
• organization = "IEEE Computational Intelligence Society",
• publisher = "IEEE Press",
• isbn13 = "978-1-4244-1823-7",
• file = "EC0029.pdf",
• DOI = "doi:10.1109/CEC.2008.4630776",
• abstract = "An improved genetic programming (GP) algorithm was developed in order to use a unified way to identify both linear and nonlinear, both time-invariant and time-varying discrete dynamic systems. 'D' operators and discrete time 'n' terminals were used to construct and evolve difference equations. Crossover operations of the improved GP algorithm were different from the conventional GP algorithm. Two levels of crossover operations were defined. A linear time-invariant system, a nonlinear time-invariant system and a time-varying system were identified by the improved GP algorithm, good models of object systems were achieved with accurate and simultaneous identification of both structures and parameters. GP generated obvious mathematical descriptions (difference equations) of object systems after expression editing, showing correct input-output relationships. It can be seen that GP is good at handling different kinds of dynamic system identification problems and is better than other artificial intelligence (AI) algorithms like neural network or fuzzy logic which only model systems as complete black boxes.",
• keywords = "genetic algorithms, genetic programming",
• notes = "WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.",

}

Genetic Programming entries for Xiao-Lei Yuan Yan Bai Ling Dong

Citations