Research of end effects in Hilbert-Huang transform based on genetic algorithm and support vector machine

The end effects of Hilbert-Huang transform are produced in the Empirical Mode Decomposition(EMD) and the Hilbert transform for Intrinsic Mode Functions(IMF), which have a badly effect on Hilbert-Huang transform. In order to overcome this problem, the multi-objective allocation Genetic Algorithm (GA) to solve the kernel parameters selection of Least Squares Support Vector Machine (LSSVM)(GLHHT) is presented in this paper. Then the LSSVM is used to predict the signal before EMD. The scheme can effectively resolve the end effects, and obtain the EVIFs with explicitly physical sense and Hilbert spectrum. The simulation results from the typical definite and practical signals demonstrate that the end effects of Hilbert Huang transform could be resolved effectively, and its effects are better than prediction methods by RBF neural network and SVM, respectively.