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.

最近提出的基于波动的弥散熵（FDE）可以区分生物医学时间序列的各种生理状态，并且通常用于生物医学领域。受到FDE理论的启发，我们重新定义了FDE和反向色散熵（RDE），并提出了基于涨落的反向色散熵（FRDE），这是FDE和RDE的一种改进方法。作为一种复杂性特征，首先将FRDE与K最近邻（KNN）结合用于信号分类，然后提出一种基于FRDE和KNN的信号分类新方法，称为FRDE-KNN。我们将色散熵（DE），置换熵（PE）和FDE与KNN相结合，分别得到DE-KNN，PE-KNN和FDE-KNN的三种分类方法，然后基于这四种分类方法进行对比实验，实验结果表明，FRDE可以表示信号的复杂度，具有较好的可分离性。FRDE-KNN具有比DE-KNN，PE-KNN和FDE-KNN更高的分类识别率，可以更好地对船舶信号和齿轮故障信号进行分类。