Fluctuation-based reverse dispersion entropy and its applications to signal classification
Fluctuation-based reverse dispersion entropy and its applications to signal classification
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基于涨落的逆色散熵及其在信号分类中的应用
DOI：
10.1016/j.apacoust.2020.107857
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发表时间：
2021-04-01
影响因子：
3.4
通讯作者：
Wang, Qing
中科院分区：
文献类型：
--
作者：
Jiao, Shangbin;Geng, Bo;Wang, Qing
最近提出的基于波动的离散熵（FDE）可以区分生物医学时间序列的各种生理状态，通常用于生物医学领域。受FDE理论的启发，我们重新定义了FDE和反向色散熵（RDE），提出了基于波动的反向色散熵（FRDE），这是FDE和RDE的改进方法。FRDE作为一种复杂度特征，首先结合k -最近邻（KNN）将其应用到信号分类中，然后提出了一种基于FRDE和KNN的新的信号分类方法，称为FRDE-KNN。将色散熵（DE）、置换熵（PE）和FDE与KNN相结合，分别得到了DE-KNN、PE-KNN和FDE-KNN三种分类方法，并基于这四种分类方法进行了对比实验，实验结果表明，FRDE能较好地表征信号的复杂性，具有较好的可分性；与DE-KNN、PE-KNN和FDE-KNN相比，FRDE-KNN具有更高的分类识别率，能更好地对船舶信号和齿轮故障信号进行分类。(C) 2020 Elsevier Ltd.版权所有。
The recently proposed fluctuation-based dispersion entropy (FDE) can distinguish various physiological states of biomedical time series, and is usually used in the field of biomedicine. Inspired by the theory of FDE, we redefine FDE and reverse dispersion entropy (RDE), and propose fluctuation-based reverse dispersion entropy (FRDE), which is an improved method of FDE and RDE. As a complexity feature, FRDE is first applied to signal classification combined with K-Nearest Neighbor (KNN), and then a novel signal classification method is proposed based on FRDE and KNN, called FRDE-KNN. We combine dispersion entropy (DE), permutation entropy (PE) and FDE with KNN to get three classification methods of DE-KNN, PE-KNN and FDE-KNN respectively, and then comparative experiments based on these four classification methods are carried out, the experimental results show that FRDE can represent the complexity of signals and have the better separability; and FRDE-KNN has higher classification recognition rate than DE-KNN, PE-KNN and FDE-KNN, which can better classify the ship signals and gear fault signals. (C) 2020 Elsevier Ltd. All rights reserved.