%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 }