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

Grigorios N. Beligiannis

doi:10.1109/TIM.2005.858573

Return

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

2005-12-01

PRE

Grigorios N. Beligiannis *

Skarlas, LV

Likothanassis, SD

K.G. Perdikouri

DOI：10.1109/TIM.2005.858573

Original

Original request for help

Save

Abstract

En 中文

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 evolutionaly multimodel 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.

Keywords:

genetic programming

evolutionary multimodel partitioning filters

biomedical data

nonlinear model identification

classification

prediction

AI Summary

Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.