Identification of Linear Time-invariant, Nonlinear and Time Varying Dynamic Systems Using Genetic Programming
Created by W.Langdon from
gp-bibliography.bib Revision:1.8081
- @InProceedings{Yuan:2008:cec,
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author = "Xiao-Lei Yuan and Yan Bai and Ling Dong",
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title = "Identification of Linear Time-invariant, Nonlinear and
Time Varying Dynamic Systems Using Genetic
Programming",
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booktitle = "2008 IEEE World Congress on Computational
Intelligence",
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year = "2008",
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editor = "Jun Wang",
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pages = "56--61",
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address = "Hong Kong",
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month = "1-6 " # jun,
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organization = "IEEE Computational Intelligence Society",
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publisher = "IEEE Press",
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isbn13 = "978-1-4244-1823-7",
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file = "EC0029.pdf",
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DOI = "doi:10.1109/CEC.2008.4630776",
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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.",
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keywords = "genetic algorithms, genetic programming",
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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