文件名称:333
- 所属分类：
- 人工智能/神经网络/遗传算法
- 资源属性：
- [PDF]
- 上传时间：
- 2013-03-23
- 文件大小：
- 181kb
- 下载次数：
- 0次
- 提 供 者：
- 张*
- 相关连接：
- 无
- 下载说明：
- 别用迅雷下载，失败请重下，重下不扣分！
介绍说明－－下载内容均来自于网络，请自行研究使用
若信号间的能量和频率比例过大，经验模式分解不能分解出正确的单一模式分量。针对这
种状况提出一种经验模式分解与独立分量相结合的信号分析方法。该方法能分离出 IMF 分量的固有特性，
消除EMD分解过后各IMF之间信息混淆问题，恢复各个单分量所丢失的信息特性，改善了经验模式分解能力不足所带来局限性，保障经验模式分解的有效性。-If the signal energy and frequency ratio is too large, the empirical mode decomposition can not be decomposed correct single-mode component. In response to this situation, combined with independent component signal analysis method is proposed empirical mode decomposition. The inherent characteristics of the isolated IMF component, eliminating the EMD decomposition after information between each IMF confuse the issue and restore the characteristics of each single component of the missing information to improve the empirical mode decomposition lack limitations protect empirical mode decomposition effectiveness.
种状况提出一种经验模式分解与独立分量相结合的信号分析方法。该方法能分离出 IMF 分量的固有特性，
消除EMD分解过后各IMF之间信息混淆问题，恢复各个单分量所丢失的信息特性，改善了经验模式分解能力不足所带来局限性，保障经验模式分解的有效性。-If the signal energy and frequency ratio is too large, the empirical mode decomposition can not be decomposed correct single-mode component. In response to this situation, combined with independent component signal analysis method is proposed empirical mode decomposition. The inherent characteristics of the isolated IMF component, eliminating the EMD decomposition after information between each IMF confuse the issue and restore the characteristics of each single component of the missing information to improve the empirical mode decomposition lack limitations protect empirical mode decomposition effectiveness.
(系统自动生成,下载前可以参看下载内容)
下载文件列表
独立分量分析方法在经验模式分解中的应用 .pdf