Inproceedings,
Genetic programming-based modeling on chaotic time series
Proceedings of the third International Conference on Machine Learning and Cybernetics (ICMLC 2004), 4, page 2347--2352. IEEE Press, (2004)
Abstract
One of the difficulties in nonlinear time series
analysis is how to reconstruct the system model from
the data series. This is mainly due to the dissipation
and "butterfly" effect of the chaotic systems. This
paper proposes a genetic programming-based modeling
(GPM) algorithm for the chaotic time series. In GPM,
genetic programming-based techniques are used to search
for appropriate model structures in the function space,
and the particle swarm optimization (PSO) algorithm is
introduced for nonlinear parameter estimation (NPE) on
dynamic model structures. In addition, the results of
nonlinear time series analysis (NTSA) are integrated
into the GPM to improve the modeling quality and the
criterion of the established models. The effectiveness
of such improvements is proved by modeling the
experiments on known chaotic time series.
