A Genetic Programming approach to inductive inference of chaotic series, with reference to Solomonoff complexity, is presented. It consists in evolving a population of mathematical expressions looking for the 'optimal' one that generates a given chaotic data series. Validation is performed on the Logistic, the Henon and the Mackey-Glass series. The method is shown effective in obtaining the analytical expression of the first two series, and in achieving very good results on the third one.
Inductive Inference of Chaotic Series by Genetic Programming: a Solomonoff-based Approach
DE FALCO I;E TARANTINO
2005
Abstract
A Genetic Programming approach to inductive inference of chaotic series, with reference to Solomonoff complexity, is presented. It consists in evolving a population of mathematical expressions looking for the 'optimal' one that generates a given chaotic data series. Validation is performed on the Logistic, the Henon and the Mackey-Glass series. The method is shown effective in obtaining the analytical expression of the first two series, and in achieving very good results on the third one.File in questo prodotto:
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