Open AccessChaotic time series prediction based on information entropy optimized parameters of phase space reconstruction
Author(s) -
Chuntao Zhang,
Qianli Ma,
Hong Peng
Publication year - 2010
Publication title -
wuli xuebao
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.199
H-Index - 47
ISSN - 1000-3290
DOI - 10.7498/aps.59.7623
Subject(s) - phase space , chaotic , computer science , embedding , entropy (arrow of time) , series (stratigraphy) , algorithm , lorenz system , statistical physics , dimension (graph theory) , mathematics , artificial intelligence , physics , paleontology , quantum mechanics , biology , pure mathematics , thermodynamics
This paper proposes a method of information entropy optimized parameters (IEOP) of pahse space recon struction. First, it establishes an information entropy optimum model in phase space for embedding dimension and delay time by using conditional entropy. It then solves these two parameters with genetic algorithm (GA) simultaneously. IEOP constructs an optimum phase space, which maintains independence of reconstruction coordinate and retains the dynamic characteristics of the original system. In the numerical simulations, results of the Lorenz system and Mackey-Glass system show that it not only determines two parameters at the same time, but also can obtains more information in the optimized phase space, there by improving the performance of chaotic time series prediction.