期刊
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
卷 54, 期 6, 页码 2184-2190出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TIM.2005.858573
关键词
genetic programming; evolutionary multimodel partitioning filters; biomedical data; nonlinear model identification; classification; prediction
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.
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