【摘要】 经验模态分解(empirical mode decomposition,简称EMD)的端点效应使得EMD分解结果产生严重失真,为了减小分解过程中产生的端点效应,将支持向量机(SVM)这一智能算法引入EMD,提出采用SVM模型解决分解中产生的端点效应问题.通过支持向量机对其原始数据两端进行延拓,以获得一个或者多个极大值和极小值.为了使端点处的延拓交得更加合理,引入粒子群(PSO)智能算法对支持向量机算法参数进行优化,使其两个端点处的数据延拓得更加准确,从而使得三次样条曲线在端点处不会发生大的摆动,实现EMD分解的固有模态函数(IMF)更加准确可靠.通过对仿真信号的研究表明,基于PSO-SVM方法的延拓方法能够很好地抑制了分解的端点效应.更多还原

【Abstract】 End effects of EMD(empirical mode decomposition) make a serious distortion of the decomposition result.In order to reduce the end effects in the process of decomposition,support vector machine (SVM) which is a kind of intelligent algorithm is combined with EMD,then a solution to the end effects problem during the course of decomposition using SVM model is proposed.Firstly,one or more extreme values are obtained by extending two endpoints of the original data with SVM.Moreover,in order to get more reasonable extension at endpoint,SVM algorithm is combined with particle swarm algorithm(PSO) to optimize the parameters,and the extension of two endpoints will be more accurate,then the end-points of cubic spline curve will not have large swing so as to achieve that intrinsic mode functions(IMF) of EMD are more accurate and reliable.Simulation results indicate that the extension method for data based on PSO-SVM method can restrain the end effects effectively.更多还原