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

In this contribution, a genetic programming based technique, which combines the ability of genetic programming 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 genetic programming technique to converge more quickly to the (near) optimal model structure. The method has all the known advantages of the evolutionary multimodel 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 VLSI implementation.