Emd Method And Applications

Extracting instantaneous characters of the signals is very important in signal processing. But defining instantaneous parameters of complicated signals especially of the non-linear and non-stationary ones is difficult, measuring the parameters of them is even more difficult. How to extract the instantaneous parameters of the non-linear and non-stationary signals making use of the data analysis methods is the emphasis the thesis will discuss, as well as how to improve the method and apply it.The main work of the dissertation includes:1. In the course of filtration, interpolation is the essential step to produce the intrinsic mode functions, and the basis of the Hilbert spectral analysis. Although Hermite interpolation works well in most cases, there are still some problems existing. It proves the validity of the spline interpolation on the base of experiments.2. There are two types of end effects in the empirical mode decomposition method: in the spline interpolation and in the Hilbert transform. The cubic spline will swing widely if the end issue is left unattended, and it will affect the veracity of the instantaneous characters' extracting. There are some methods to solve the problem, they have confined the large swings successfully. It has brought forward polynomial fitting algorithm, and has proved its correctness and superiority making use of datum and theories.3. Carrying out the improved empirical mode decomposition method utilizing the algorithm.4. Realizing the application of the empirical mode decomposition in the instantaneous characters extraction of the non-linear and non-stationary signals. In the meanwhile, it has proved that it is feasible to realize the similar pattern matching of the stocks' forecasting, if the empirical mode decomposition and the forward propagation learning algorithm of multi layered neural networks with feed back connections are related.