Аннотация
This paper proposes a Genetic Programming-Based
Modeling (GPM) algorithm on chaotic time series. GP is
used here to search for appropriate model structures in
function space, and the Particle Swarm Optimization
(PSO) algorithm is used for Nonlinear Parameter
Estimation (NPE) of dynamic model structures. In
addition, GPM integrates the results of Nonlinear Time
Series Analysis (NTSA) to adjust the parameters and
takes them as the criteria of established models.
Experiments showed the effectiveness of such
improvements on chaotic time series modeling.
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