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.
IEEE Transactions on Instrumentation and Measurement
Nummer
6
Seiten
2184--2190
Band
54
issn
0018-9456
size
7 pages
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
%0 Journal Article
%1 Beligiannis:2005:tIM
%A Beligiannis, Grigorios N.
%A Skarlas, Lambros V.
%A Likothanassis, Spiridon D.
%A Perdikouri, Katerina G.
%D 2005
%J IEEE Transactions on Instrumentation and Measurement
%K algorithms, biomedical complex data dynamical evolutionary filters, genetic identification, medical model multimodel nonlinear partitioning processing, programming, signal structure systems
%N 6
%P 2184--2190
%R 10.1109/TIM.2005.858573
%T Nonlinear model structure identification of complex
biomedical data using a genetic-programming-based
technique
%V 54
%X 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.
@article{Beligiannis:2005:tIM,
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.},
added-at = {2008-06-19T17:35:00.000+0200},
author = {Beligiannis, Grigorios N. and Skarlas, Lambros V. and Likothanassis, Spiridon D. and Perdikouri, Katerina G.},
biburl = {https://www.bibsonomy.org/bibtex/259ee993cfffb56e5b99b4c05ac70cd30/brazovayeye},
doi = {10.1109/TIM.2005.858573},
interhash = {f4e296019f43842f943984acc36cfc34},
intrahash = {59ee993cfffb56e5b99b4c05ac70cd30},
issn = {0018-9456},
journal = {IEEE Transactions on Instrumentation and Measurement},
keywords = {algorithms, biomedical complex data dynamical evolutionary filters, genetic identification, medical model multimodel nonlinear partitioning processing, programming, signal structure systems},
month = {December},
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},
number = 6,
pages = {2184--2190},
size = {7 pages},
timestamp = {2008-06-19T17:36:23.000+0200},
title = {Nonlinear model structure identification of complex
biomedical data using a genetic-programming-based
technique},
volume = 54,
year = 2005
}