期刊
PHYSICS LETTERS A
卷 380, 期 5-6, 页码 696-706出版社
ELSEVIER SCIENCE BV
DOI: 10.1016/j.physleta.2015.11.036
关键词
Rare chaotic flows; Hidden attractor; Fuzzy functions; Recurrent structure; Long term prediction
The nonlinear and dynamic accommodating capability of time domain models makes them a useful representation of chaotic time series for analysis, modeling and prediction. This paper is devoted to the modeling and prediction of chaotic time series with hidden attractors using a nonlinear autoregressive model with exogenous inputs (NARX) based on a novel recurrent fuzzy functions (RFFs) approach. Case studies of recently introduced chaotic systems with hidden attractors plus classical chaotic systems demonstrate that the proposed modeling methodology exhibits better prediction performance from different viewpoints (short term and long term) compared to some other existing methods. (c) 2015 Elsevier B.V. All rights reserved.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据