Research And Improvement Of The EMD Experience Sieve Method

Empirical Mode Decomposition(EMD) is a time-frequency analysis method.According to the characteristies of input signals,this method can adaptively decompose theoriginal signal to a sum of some intrinsic mode functions without any priorknowledge.This method is often used to deal with the nonlinear and non-stationarysignal.After years of process development,this method has its unique superiority in thefield of non-stationary signal.And has a great theoretical research value and a broadapplication prospect.In this passage,there has made a further research on the whole theory ofEMD.Summarized the existing experience sieve method.Make a further research on thestable point average sieve method.And then simulate this method. At the same time,thepassage has made a lot of research work to solve the problems of boundary.The maincontents of the paper are as follows:Introduced the EMD theory,elaborated the basic principle,properties and mainproblems of the EMD,summarized the development status quo,application field andresearch hotspot of the EMD.Studied several experience sieve method has appeared in theliterature,summarized the advantages and disadvantages of the sieve method.Furtherlystudied the stable point average sieve method.This method is on the basis of the principleof quasi stable point,directly fitting the signal average curve to analyze the signal.Finallyby the simulation experiment,the stable point average sieve method is proved to becorrect.Deeply analyzed the importance of the boundary problem of the EMD,furtherlystudied the boundary of the EMD algorithm.Reviewed several existing boundaryprocessing algorithms,and given the corresponding physical meaning,evaluated theadvantages and disadvantages of several kinds of border processing algorithms.This paperstudied the eigen wave matching method based on the idea of waveform matching.Bycomparison with other method,instruct and verify the progress of the new boundaryprocessing algorithms. This method has a high theoretical and practical value.