【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
【机 构】
：
北京理工大学爆炸科学与技术国家重点实验室,华北科技学院,
论文部分内容阅读
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.
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