[1]LI Xue-yao,ZHANG Ru-bo,WANG Wu.End effects processing in HHT based on support vector regression machines[J].CAAI Transactions on Intelligent Systems,2007,2(3):39-44.
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End effects processing in HHT based on support vector regression machines

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 2 Number of periods: 2007 3 Page number: 39-44 Column: 学术论文—智能系统 Public date: 2007-06-25
Title:
End effects processing in HHT based on support vector regression machines
Author(s):
LI Xue-yao;  ZHANG Ru-bo;  WANG Wu
College of Computer Science and Technology, Harbin Engineering University, Harbin 150001, China
Keywords:
end effects;  HilbertHuang transform;  support vector regression machines;  particle swarm optimization
CLC:
TP18
DOI:
-
Abstract:
In order to better restrain end effects in the HilbertHuang transform (HHT), a time sequence prediction technique is proposed based on support vector regression machines to improve time series prediction. In the application of support vector regression machines (SVRM), parameter selection has a great influence on generalization performance. So in this paper, the influence of parameters on the generalization of SVRM is discussed, and then a particle swarm optimization (PSO) algorithm is used to optimize parameters. Using this method, SVRM can select optimal parameters selfadaptively, so that higher generalization performance is obtained in applications, prediction accuracy is improved at both ends and the end effects are restrained effectively. In contrast to the neural network methods and HHTDPS proposed by Huang et al., the end effects can be restrained better and the Intrinsic Mode Functions have less distortion. Experiments show that this method can solve the problem of selecting parameters properly.
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