解决经验模态分解末端效应的新方法

Leitao Zhang, Huanguo Chen, Jianmin Li, Wenhua Chen
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引用次数: 2

摘要

1 .基金项目:国家自然科学基金(50805132)和教育部博士授予点基金(200803380001)资助项目。摘要针对经验模态分解(EMD)的末端效应问题,提出了末端筛选方法。在EMD方法的本征模态函数(IMF)筛选过程中,加入了一个包括端点效应判断和端点筛选的过程,不同于传统的通过指示或预测端点来处理问题的思路。该方法大大提高了IMF的精度。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
New Method to Solve the End Effect of Empirical Mode Decomposition
1 Fund Project: Project is provided by State Natural Sciences Fund ( 50805132) and Doctor Conferring Points Foundation of Ministry of Education under the grants (200803380001). Abstract—The end sifting method has been proposed to solve the end effect problem of Empirical Mode Decomposition (EMD). During the Intrinsic Mode Function (IMF) sifting process by EMD method, a procedure including end effect judgment and end sifting, which is different from the traditional idea dealing with the problem by dictating or predicting an end point, is added. This method has greatly improved the precision of IMF.
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