Use of Genetic Programming In The Identification Of Rational Model Structures

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

@InProceedings{rodriguez-vazquez:2000:GPirms,
  author =       "Katya Rodriguez-Vazquez and Peter J. Fleming",
  title =        "Use of Genetic Programming In The Identification Of
                 Rational Model Structures",
  booktitle =    "Genetic Programming, Proceedings of EuroGP'2000",
  year =         "2000",
  editor =       "Riccardo Poli and Wolfgang Banzhaf and 
                 William B. Langdon and Julian F. Miller and Peter Nordin and 
                 Terence C. Fogarty",
  volume =       "1802",
  series =       "LNCS",
  pages =        "181--192",
  address =      "Edinburgh",
  publisher_address = "Berlin",
  month =        "15-16 " # apr,
  organisation = "EvoNet",
  publisher =    "Springer-Verlag",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "3-540-67339-3",
  URL =          "http://www.springerlink.com/openurl.asp?genre=article&issn=0302-9743&volume=1802&spage=181",
  DOI =          "doi:10.1007/978-3-540-46239-2_13",
  abstract =     "This paper demonstrates how genetic programming can be
                 used for solving problems in the field of non-linear
                 system identification of rational models. By using a
                 two-tree structure rather than introducing the division
                 operator in the function set, this genetic programming
                 approach is able to determine the true model structure
                 of the system under investigation. However, unlike use
                 of the polynomial, which is linear in the parameters,
                 use of rational model is non-linear in the parameters
                 and thus noise terms cannot be estimated properly. By
                 means of a second optimisation process (real-coded GA)
                 which has the aim of tunning the coefficients to the
                 true values, these parameters are then correctly
                 computed. This approach is based upon the well-known
                 NARMAX model representation, widely used in non-linear
                 system identification.",
  notes =        "EuroGP'2000, part of \cite{poli:2000:GP}",
}

Genetic Programming entries for Katya Rodriguez-Vazquez Peter J Fleming

Citations