EMD endpoint effect suppression method based on LSTM network

Abstract

The invention belongs to the technical field of signal processing, and particularly relates to an EMD endpoint effect suppression method based on an LSTM network. According to the method, the prediction of the LSTM network on the signal and the application of EMD decomposition in the single-channel blind source separation technology are effectively combined, and the time step length of the LSTM network is searched according to the characteristics of the signal, so that the prediction precision is effectively improved, the problem of end effect occurring in the EMD method in the single-channel blind source separation is effectively solved, and the blind source separation efficiency is improved. and a good effect is achieved in solving the problem of sound separation in a complex factory environment. The method can effectively solve the endpoint effect problem in nonlinear non-stationary time sequence analysis, and can be applied to the separation problem of single-channel blind source mixed signals.

Description

technical field [0001] The invention belongs to the technical field of signal processing, and in particular relates to an LSTM network-based EMD endpoint effect suppression method. Background technique [0002] Blind source separation technology refers to the process of separating each source signal by only using the observed signal according to the statistical characteristics of the source signal when the transmission channel and the source signal are unknown. In fact, most of the collected signals are single-channel signals, and single-channel blind source separation is an extreme case of underdetermined blind source separation. It only uses the characteristic information of single-channel observation signals to separate multi-channel source signals. It is very difficult to solve. In 1998, Norden E.Huang of NASA firstly proposed a signal processing method of empirical mode decomposition for a series of time series data, and then Hilbert transform for each component. The t...

AI Technical Summary

Problems solved by technology

The endpoint effect in cubic spline interpolation and Hilbert transform has a great influence on the time-frequency analysis method based on EMD. If this problem is not handled properly, the effect of this time-frequency analysis method will be affected or invalidated.

However, in its application, the selection of LSTM network time step parameters has a great influence on the prediction results, so determining the LSTM time step to improve the prediction performance of data is the key issue to suppress the EMD endpoint effect

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