Nonlinear model structure identification of complex biomedical data using a genetic-programming-based technique

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@Article{Beligiannis:2005:tIM,
  title =        "Nonlinear model structure identification of complex
                 biomedical data using a genetic-programming-based
                 technique",
  author =       "Grigorios N. Beligiannis and Lambros V. Skarlas and 
                 Spiridon D. Likothanassis and Katerina G. Perdikouri",
  journal =      "IEEE Transactions on Instrumentation and Measurement",
  year =         "2005",
  volume =       "54",
  number =       "6",
  pages =        "2184--2190",
  month =        dec,
  keywords =     "genetic algorithms, genetic programming, medical
                 signal processing, nonlinear dynamical systems complex
                 biomedical data identification, evolutionary multimodel
                 partitioning filters, nonlinear model structure",
  DOI =          "doi:10.1109/TIM.2005.858573",
  ISSN =         "0018-9456",
  size =         "7 pages",
  abstract =     "In this contribution, a genetic programming (GP)-based
                 technique, which combines the ability of GP to explore
                 both automatically and effectively, the whole set of
                 candidate model structures and the robustness of
                 evolutionary multimodel partitioning filters, is
                 presented. The method is applied to the nonlinear
                 system identification problem of complex biomedical
                 data. Simulation results show that the algorithm
                 identifies the true model and the true values of the
                 unknown parameters for each different model structure,
                 thus assisting the GP technique to converge more
                 quickly to the (near) optimal model structure. The
                 method has all the known advantages of the evolutionary
                 multi model partitioning filters, that is, it is not
                 restricted to the Gaussian case; it is applicable to
                 on-line/adaptive operation and is computationally
                 efficient. Furthermore, it can be realized in a
                 parallel processing fashion, a fact which makes it
                 amenable to very large scale integration
                 implementation.",
  notes =        "Fig. 3. Plot of the real (solid line) versus the
                 predicted (dashed line) values for an epoch consisting
                 of 300 samples of an epileptic MEG (MEG measured in pT
                 = 10 T).

                 Fig. 4. Plot of the real (solid line) versus the
                 predicted (dashed line) values of an f-MCG in a normal
                 pregnancy (f-MCG measured in pT = 10 T).

                 TABLE II ABILITY OF THE ESTIMATED NONLINEAR MODEL IN
                 PREDICTING ABNORMAL PREGNANCIES",
}

Genetic Programming entries for Grigorios N Beligiannis Lambros V Skarlas Spiridon D Likothanassis Katerina G Perdikouri

Citations