Nonlinear model structure identification of complex
biomedical data using a genetic-programming-based
technique
IEEE Transactions on Instrumentation and Measurement, 54 (6):
2184--2190 (December 2005)DOI: 10.1109/TIM.2005.858573
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.
