Nonlinear model structure identification of complex biomedical data using a geneticprogrammingbased technique
Created by W.Langdon from
gpbibliography.bib Revision:1.4685
 @Article{Beligiannis:2005:tIM,

title = "Nonlinear model structure identification of complex
biomedical data using a geneticprogrammingbased
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 = "21842190",

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 = "00189456",

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
online/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 fMCG in a normal
pregnancy (fMCG 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