Open AccessParameter joint estimation of phase space reconstruction in chaotic time series based on radial basis function neural networks
Author(s) -
Diyi Chen,
Ye Liu,
Xiaoyi Ma
Publication year - 2012
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.61.100501
Subject(s) - chaotic , radial basis function , computer science , series (stratigraphy) , artificial neural network , phase space , dimension (graph theory) , lorenz system , basis (linear algebra) , radial basis function network , embedding , joint (building) , function (biology) , algorithm , time series , mathematics , artificial intelligence , machine learning , physics , architectural engineering , paleontology , geometry , evolutionary biology , engineering , biology , thermodynamics , pure mathematics
In this paper, we propose a joint estimation method of two parameters for phase space reconstruction in chaotic time series, based on radial basis function (RBF) neural networks. And we obtain the best estimation values, according to some objective standards. Furthermore, The single-step and multi-step RBF prediction model is used to estimate the best embedding dimension and delay time, and Lorenz system is selected as an example. Finally, the estimation values are tested in the original model. The simulations show that we can obtain the best estimation values through the method, and the prediction accuracy is significantly improved.