Nonlinear mode decomposition: A noise-robust, adaptive decomposition method
Nonlinear mode decomposition: A noise-robust, adaptive decomposition method
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DOI：
10.1103/physreve.92.032916
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发表时间：
2015-09-29
影响因子：
2.4
通讯作者：
Stefanovska, Aneta
中科院分区：
文献类型：
--
作者：
Iatsenko, Dmytro;McClintock, Peter V. E.;Stefanovska, Aneta
从复杂系统发出的信号通常由不同振荡的混合物组成，为了进行可靠的分析，应该将这些振荡彼此分离并与不可避免的噪声背景分离。在这里，我们介绍了一个自适应分解工具-非线性模式分解（NMD）-它将给定的信号分解成一组物理上有意义的振荡的任何波形，同时消除噪声。NMD是基于强大的组合的时间-频率分析技术，其中，连同自适应选择的参数，使其非常噪声鲁棒性和替代数据测试，用于识别相互依赖的振荡和区分确定性随机活动。我们说明了NMD在模拟信号和真实的信号中的应用，并证明了其相对于其他方法（例如（系综）经验模式分解、Karhunen-Loeve展开和独立分量分析）的定性和定量优越性。我们指出，NMD可能适用于许多不同的研究领域，如物理学、金融和生命科学。运行NMD所需的MATLAB代码可以免费下载。
The signals emanating from complex systems are usually composed of a mixture of different oscillations which, for a reliable analysis, should be separated from each other and from the inevitable background of noise. Here we introduce an adaptive decomposition tool-nonlinear mode decomposition (NMD)-which decomposes a given signal into a set of physically meaningful oscillations for any wave form, simultaneously removing the noise. NMD is based on the powerful combination of time-frequency analysis techniques-which, together with the adaptive choice of their parameters, make it extremely noise robust-and surrogate data tests used to identify interdependent oscillations and to distinguish deterministic from random activity. We illustrate the application of NMD to both simulated and real signals and demonstrate its qualitative and quantitative superiority over other approaches, such as (ensemble) empirical mode decomposition, Karhunen-Loeve expansion, and independent component analysis. We point out that NMD is likely to be applicable and useful in many different areas of research, such as geophysics, finance, and the life sciences. The necessary MATLAB codes for running NMD are freely available for download.