Denoising Nonlinear Time Series Using Singular Spectrum Analysis and Fuzzy Entropy

被引：0
|
作者：
江剑 [1 ]
谢洪波 [2 ]
机构：
[1] School of Mechanical Engineering,Nanjing University of Science and Technology
[2] ARC Centre of Excellence for Mathematical and Statistical Frontiers,Queensland University of Technology
来源：
Chinese Physics Letters | 2016年 / 10期
关键词：
of; on; or; in; Denoising Nonlinear Time Series Using Singular Spectrum Analysis and Fuzzy Entropy; NLP; is;
D O I：
暂无
中图分类号：
TN911.7 [信号处理]; O211.61 [平稳过程与二阶矩过程];
学科分类号：
020208 ; 070103 ; 0711 ; 0714 ; 080401 ; 080402 ;
摘要：
We present a hybrid singular spectrum analysis(SSA) and fuzzy entropy method to filter noisy nonlinear time series.With this approach,SSA decomposes the noisy time series into its constituent components including both the deterministic behavior and noise,while fuzzy entropy automatically differentiates the optimal dominant components from the noise based on the complexity of each component.We demonstrate the effectiveness of the hybrid approach in reconstructing the Lorenz and Mackey-GIass attractors,as well as improving the multi-step prediction quality of these two series in noisy environments.
引用
收藏
页码：23 / 27
页数：5
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