...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Applied mathematics and computation >An improved empirical mode decomposition method based on the cubic trigonometric B-spline interpolation algorithm
【24h】

An improved empirical mode decomposition method based on the cubic trigonometric B-spline interpolation algorithm

机译：一种基于立方三角函数B样条插值算法的改进的经验模式分解方法

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Empirical mode decomposition (EMD) is a new method presented recently for analyzing nonlinear and non-stationary signals. Its basic idea is to decompose the signal into a series of complete orthogonal intrinsic mode functions (IMEs) based on the local characteristics of the signal in time domain. The key step of EMD is to use the cubic spline interpolation to connect the maximum and minimum values of the signals into upper and lower envelopes respectively, and then calculate the mean values of upper and lower envelopes. Based on the cubic trigonometric B-spline interpolation algorithm, a new improved method for EMD is proposed named CTB-EMD in this paper. In this method, the interpolation curve is more flexible because of the adjustability of shape of the cubic trigonometric B-splines curve. Thus, the overshoot and undershoot problems in the cubic spline interpolation curve can be avoided, and then the decomposition of the signal is more accurate and effect. Through numerical experiments, we compare the effect of this method with other methods on decomposing simulation signals and real signals. Experimental results show that this method can decompose signals more effectively and accurately. (C) 2018 Elsevier Inc. All rights reserved.
机译：经验模式分解（EMD）是最近呈现的一种新方法，用于分析非线性和非静止信号。其基本思想是基于时域中信号的局部特征将信号分解为一系列完整的正交内部模式功能（IME）。 EMD的关键步骤是使用立方样条插值，分别将信号的最大值和最小值连接到上下信封中，然后计算上下信封的平均值。基于立方三角性B样条插值算法，提出了一种新的EMD改进方法，提出了本文的CTB-EMD。在该方法中，由于立方三角形B样条曲线的形状的可调节性，插值曲线更加灵活。因此，可以避免立方样条插值曲线中的过冲和下冲问题，然后信号的分解更准确，效果。通过数值实验，我们将该方法与用于分解模拟信号和实际信号的其他方法的效果进行比较。实验结果表明，该方法可以更有效和准确地分解信号。 （c）2018年Elsevier Inc.保留所有权利。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号