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Synchrosqueezed wavelet transforms: An empirical mode decomposition-like tool

机译:同步压缩的小波变换:一种类似于经验模式分解的工具

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The EMD algorithm is a technique that aims to decompose into their building blocks functions that are the superposition of a (reasonably) small number of components, well separated in the time-frequency plane, each of which can be viewed as approximately harmonic locally, with slowly varying amplitudes and frequencies. The EMD has already shown its usefulness in a wide range of applications including meteorology, structural stability analysis, medical studies. On the other hand, the EMD algorithm contains heuristic and ad hoc elements that make it hard to analyze mathematically. In this paper we describe a method that captures the flavor and philosophy of the EMD approach, albeit using a different approach in constructing the components. The proposed method is a combination of wavelet analysis and reallocation method. We introduce a precise mathematical definition for a class of functions that can be viewed as a superposition of a reasonably small number of approximately harmonic components, and we prove that our method does indeed succeed in decomposing arbitrary functions in this class. We provide several examples, for simulated as well as real data.
机译:EMD算法是一种旨在将功能分解为它们的构件的技术,这些构件是(合理地)少量组件的叠加,这些组件在时频平面中很好地分离,每个组件都可以在局部被视为近似谐波,缓慢变化的幅度和频率。 EMD已经在各种应用中显示了其有用性,包括气象学,结构稳定性分析,医学研究。另一方面,EMD算法包含启发式和临时性元素,因此很难进行数学分析。在本文中,我们描述了一种捕获EMD方法的风格和原理的方法,尽管在构造组件时使用了不同的方法。提出的方法是小波分析和重新分配方法的结合。我们为一类函数引入了精确的数学定义,可以将其看作是相当少量的近似谐波分量的叠加,并且证明了我们的方法确实能够成功分解此类中的任意函数。我们提供了一些示例,分别用于模拟和真实数据。

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