Empirical mode decomposition method and system for adaptive binary and conjugate shielding network

Abstract

The invention provides a signal processing method. The method comprises the steps of firstly, decomposing an original signal by utilizing an empirical mode decomposition method, obtaining an intrinsic mode function, and calculating n-order conjugate shielding functions; secondly, adding the n-order conjugate shielding functions to the original signal to generate a modulation signal, performing decomposition through the empirical mode decomposition method to generate n-order modulation mode functions; and performing addition on the n-order modulation mode functions, performing division by the number of the n-order conjugate shielding functions to generate n-order model functions, and repeating the process until the n-order model functions are monotonic functions, wherein the mode functions of different orders are non-steady-state and nonlinear periodic oscillation signals of the original signal in different frequency ranges. According to the method, the advantages of the existing mode decomposition method are integrated but the mixed mode problem caused by intermittent disturbance in original data is excluded, so that an analytic numerical value of the original signal can be directly and quickly obtained.

Description

technical field [0001] The present invention relates to a signal processing method and system for empirical mode decomposition of adaptive binary and conjugate shielding grids, especially a low calculation amount, noiseless and fast binary decomposition of nonlinear and unstable A method and a system for saving state data and reducing calculation time. Background technique [0002] Empirical Mode Decomposition (EMD) is a calculation method based on Hilbert-Huang Transformation (HHT), which has been widely used in nonlinear or unsteady signal processing in recent years , such as seismic analysis, biomedical signal calculation, etc. [0003] Through empirical mode decomposition, the original signal can be sequentially decomposed into corresponding functions from high frequency to low frequency, which has the characteristics of Intrinsic Mode Function (IMF). However, EMD has a major disadvantage when it is used for filtering, which is the mode mixing effect. In the process o...

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Problems solved by technology

However, empirical mode decomposition has a major disadvantage when used for filtering, that is the mode mixing effect

In the process of empirical mode decomposition, there will be mixed-mode problems, because some systems have intermittent signals (intermittence), so that the empirical mode decomposition cannot correctly calculate the signals of the same scale. Therefore, in the same essential mode function There will be a mixture of modes of different scales, or modes of the same scale appear in different intrinsic mode functions

[0004] Although in 2009, Dr. Huang E et al. proposed the overall empirical mode decomposition method (Ensemble Empirical Mode Decomposition, EEMD) assisted by noise, adding white noise (white noise) to solve the mixed-mode problem, although it can solve the mixed-mode problem , but the computational complexity is more than the traditional empirical mode decomposition method by a multiple of the overall quantity, and it is difficult to apply to signals that require real-time calculation or a large amount of data, which limits the use of empirical mode decomposition method

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