基于Labview的非平稳信号经验模态分解算法

Ruth Moly Benjamin, X. Anitha mary, Deepika Nareti, Bavithra B
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引用次数: 0

摘要

本文提出了一种更有效地分析时域非线性信号的方法。使用改进的经验模态分解(EMD)将噪声方差作为信号去噪的阈值。利用EMD对时域进行分解。由于imf随时间不同,因此不假定信号具有平稳性。因此,与小波和傅立叶等其他方法相比,它更适合于非线性信号。这样,EMD是一种更有吸引力的分析复杂系统信号的方法。对于自动化和测量,LabVIEW是一个功能强大且适应性强的分析和仪器仪表软件系统。由于它是基于软件的,它确保了比标准实验室仪器更大的灵活性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Labview Based Empirical Mode Type Signal Decomposition Algorithm For Non-stationary signals
This paper proposes a method to analyze non-linear signals in time domain more effectively. The noise variance is used as the threshold for denoising a signal using an improved empirical mode decomposition (EMD).The time domain is decomposed using EMD. Stationarity of a signal is not assumed as the IMFs differ with time. Therefore it is more apt for nonlinear signals compared to other methods such as Wavelets and Fourier. This way EMD is a more attracting method for analyzing signals from complex systems. For automation and measurement LabVIEW is a powerful and adaptable analysis and instrumentation software system. Since it is software based it ensures greater flexibility than standard laboratory instruments.
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