Complementary integrated empirical mode decomposition method for adaptive determination of decomposition parameters

Abstract

The present invention discloses a complementary integrated empirical mode decomposition method for adaptive determination of decomposition parameters. The method is characterized in that: complementary integrated empirical mode decomposition is performed on original signals by adopting a method of gradually increasing the integrated average number of times m and gradually increasing the amplituderatio coefficient k, so that optimal decomposition parameters of the complementary integrated empirical mode decomposition can be adaptively determined, modal aliasing can be effectively suppressed, different decomposition parameters can be adaptively determined for different original signals, decomposition accuracy and computational efficiency of complementary integrated empirical mode decomposition are ensured, and the intrinsic modal components obtained by decomposition are more accurate and can more effectively represent the physical meaning of the original signals.

Description

technical field [0001] The invention relates to the technical field of signal analysis and processing, in particular to a complementary integrated empirical mode decomposition method for adaptive determination of decomposition parameters. Background technique [0002] The main characteristic of the non-stationary nonlinear signal is its time-varying nature, and its frequency is transient. The Empirical Mode Decomposition (EMD) method is based on the local time-varying characteristics of the signal for adaptive time-frequency decomposition, and the data is expressed by the basis functions of variable amplitude and variable frequency decomposed from itself, which overcomes the The traditional method uses meaningless harmonic components to represent the shortcomings of non-stationary nonlinear signals. However, problems such as mode mixing seriously affect the quality of EMD decomposition. To suppress mode aliasing, Ensemble Empirical Mode Decomposition (EEMD) introduces nois...

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Problems solved by technology

In the CEEMD algorithm, if the amplitude of the white noise added to the original signal is too large, false modal components will be generated during the decomposition process; if the amplitude of the added noise is too small, it may not be enough to cause the local extremum point of the original signal change, does not solve the modal aliasing problem

If the number of integration averages m is too large, the calculation amount of decomposition will increase, and the timeliness of decomposition will deteriorate, while if m is too small, the influence of white noise on the decomposition quality cannot be eliminated.

In the existing technology, when using the traditional CEEMD method to process nonlinear and non-stationary signals, it is impossible to adaptively obtain the parameters of the ratio coefficient k and the integrated average number m of adding noise, so the results of processing nonlinear and non-stationary signals have certain limitations. the blindness of

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