希尔伯特-黄变换的Matlab程序

上传者: water_diver | 上传时间: 2019-12-21 21:35:53 | 文件大小: 98KB | 文件类型: zip
希尔伯特-黄变换(Hilbert-Huang Transform, HHT)是一种强大的信号分析方法,由物理学家希尔伯特和黄旭华共同提出。它结合了经验模态分解(Empirical Mode Decomposition, EMD)和希尔伯特变换,特别适用于非线性、非平稳信号的处理。在Matlab中实现HHT程序,可以为科研和工程领域提供强大的工具,比如在地震学、生物医学、机械工程、金融等领域有着广泛的应用。 Gabriel.Rilling编写的这个程序包含了EMD的基本算法,这是一个自适应的数据分解过程。EMD通过迭代地将原始信号分离成一系列内在模态函数(Intrinsic Mode Functions, IMF),这些IMF分别对应信号的不同频率成分。EMD的核心步骤包括:sifting过程,即不断对信号的局部最大值和最小值进行平均,直至满足IMF的定义条件。 在处理信号的端点效应时,常常会遇到问题,因为EMD在边界处可能会产生不理想的振荡。为了改善这种情况,Gabriel.Rilling的程序采用了“镜像方法”。这种技术是通过在信号的两端复制一部分数据,从而在分析过程中扩大信号的长度,有效减少因端点引起的误差。镜像方法对于确保IMF的正确提取至关重要,特别是在处理实际数据时,能够提高结果的准确性和稳定性。 希尔伯特变换则是EMD后的下一步,用于计算每个IMF的瞬时频率和振幅。希尔伯特变换提供了一个复分析的角度,通过构造一个与原始信号相位相关的辅助函数,即希尔伯特谱,可以直观地揭示信号的瞬时特性。这在分析非线性系统和非平稳过程时具有显著优势,因为它允许我们动态地追踪信号的频率变化。 在Gabriel.Rilling的程序包`package_emd`中,可能包含以下文件: 1. EMD主函数:实现了EMD算法的主程序,可能包括输入信号处理、IMF的提取和端点效应的修正。 2. 希尔伯特变换函数:对提取的IMF进行希尔伯特变换,得到瞬时频率和振幅。 3. 示例数据和脚本:演示如何使用该程序处理特定信号的示例。 4. 辅助函数:可能包括用于数据预处理、可视化或性能评估的辅助工具。 了解并掌握HHT在Matlab中的应用,对于理解非线性、非平稳信号的分析具有重要意义。通过学习和使用Gabriel.Rilling的程序,研究者和工程师可以更深入地探索这些复杂信号的隐藏特征,并可能发现新的应用领域。在实际应用中,用户应根据具体需求调整参数,以优化分解效果,并结合希尔伯特变换得到有价值的瞬时信息。

文件下载

资源详情

[{"title":"( 72 个子文件 98KB ) 希尔伯特-黄变换的Matlab程序","children":[{"title":"package_emd","children":[{"title":"install_emd.m <span style='color:#111;'> 1.53KB </span>","children":null,"spread":false},{"title":"bugfix.sh <span style='color:#111;'> 216B </span>","children":null,"spread":false},{"title":"ls-R <span style='color:#111;'> 1004B </span>","children":null,"spread":false},{"title":"EMDs","children":[{"title":"emd_online.m <span style='color:#111;'> 25.70KB </span>","children":null,"spread":false},{"title":"src","children":[{"title":"clocal_mean2.h <span style='color:#111;'> 752B </span>","children":null,"spread":false},{"title":"cemdc_fix.c <span style='color:#111;'> 4.13KB </span>","children":null,"spread":false},{"title":"cemdc2.c <span style='color:#111;'> 4.87KB </span>","children":null,"spread":false},{"title":"cextr.c <span style='color:#111;'> 14.37KB </span>","children":null,"spread":false},{"title":"clocal_mean2.c <span style='color:#111;'> 4.79KB </span>","children":null,"spread":false},{"title":"interpolation.c <span style='color:#111;'> 2.06KB </span>","children":null,"spread":false},{"title":"cemdc2_fix.c <span style='color:#111;'> 4.11KB </span>","children":null,"spread":false},{"title":"clocal_mean.c <span style='color:#111;'> 4.97KB </span>","children":null,"spread":false},{"title":"local_mean.c <span style='color:#111;'> 2.77KB </span>","children":null,"spread":false},{"title":"io_fix.c <span style='color:#111;'> 6.04KB </span>","children":null,"spread":false},{"title":"emd_complex.c <span style='color:#111;'> 508B </span>","children":null,"spread":false},{"title":"extr.c <span style='color:#111;'> 10.23KB </span>","children":null,"spread":false},{"title":"extr.h <span style='color:#111;'> 674B </span>","children":null,"spread":false},{"title":"io.h <span style='color:#111;'> 1.14KB </span>","children":null,"spread":false},{"title":"local_mean.h <span style='color:#111;'> 710B </span>","children":null,"spread":false},{"title":"emdc.c <span style='color:#111;'> 4.60KB </span>","children":null,"spread":false},{"title":"emdc_fix.c <span style='color:#111;'> 3.43KB </span>","children":null,"spread":false},{"title":"cemdc.c <span style='color:#111;'> 4.85KB </span>","children":null,"spread":false},{"title":"cextr.h <span style='color:#111;'> 770B </span>","children":null,"spread":false},{"title":"cio.h <span style='color:#111;'> 1.16KB </span>","children":null,"spread":false},{"title":"io_fix.h <span style='color:#111;'> 1.07KB </span>","children":null,"spread":false},{"title":"clocal_mean.h <span style='color:#111;'> 790B </span>","children":null,"spread":false},{"title":"cio_fix.c <span style='color:#111;'> 6.77KB </span>","children":null,"spread":false},{"title":"io.c <span style='color:#111;'> 6.86KB </span>","children":null,"spread":false},{"title":"interpolation.h <span style='color:#111;'> 468B </span>","children":null,"spread":false},{"title":"cio_fix.h <span style='color:#111;'> 1.09KB </span>","children":null,"spread":false},{"title":"cio.c <span style='color:#111;'> 7.38KB </span>","children":null,"spread":false},{"title":"emd_complex.h <span style='color:#111;'> 512B </span>","children":null,"spread":false}],"spread":false},{"title":"emdc.m <span style='color:#111;'> 2.23KB </span>","children":null,"spread":false},{"title":"cemdc_fix.m <span style='color:#111;'> 2.25KB </span>","children":null,"spread":false},{"title":"cemdc2_fix.m <span style='color:#111;'> 2.26KB </span>","children":null,"spread":false},{"title":"emd.m <span style='color:#111;'> 21.75KB </span>","children":null,"spread":false},{"title":"cemdc2.m <span style='color:#111;'> 2.31KB </span>","children":null,"spread":false},{"title":"make_emdc.m <span style='color:#111;'> 1.33KB </span>","children":null,"spread":false},{"title":"cemdc.m <span style='color:#111;'> 2.30KB </span>","children":null,"spread":false},{"title":"emdc_fix.m <span style='color:#111;'> 2.09KB </span>","children":null,"spread":false},{"title":"emd_local.m <span style='color:#111;'> 9.67KB </span>","children":null,"spread":false}],"spread":false},{"title":"utils","children":[{"title":"cemd_disp.m <span style='color:#111;'> 2.49KB </span>","children":null,"spread":false},{"title":"io.m <span style='color:#111;'> 504B </span>","children":null,"spread":false},{"title":"dirstretch.m <span style='color:#111;'> 1.22KB </span>","children":null,"spread":false},{"title":"hhspectrum.m <span style='color:#111;'> 1.41KB </span>","children":null,"spread":false},{"title":"findtag.m <span style='color:#111;'> 1.02KB </span>","children":null,"spread":false},{"title":"extr.m <span style='color:#111;'> 1.89KB </span>","children":null,"spread":false},{"title":"disp_hhs.m <span style='color:#111;'> 1.55KB </span>","children":null,"spread":false},{"title":"hastag.m <span style='color:#111;'> 659B </span>","children":null,"spread":false},{"title":"addtag.m <span style='color:#111;'> 791B </span>","children":null,"spread":false},{"title":"plot3c.m <span style='color:#111;'> 798B </span>","children":null,"spread":false},{"title":"toimage.m <span style='color:#111;'> 2.93KB </span>","children":null,"spread":false},{"title":"emd_visu.m <span style='color:#111;'> 3.14KB </span>","children":null,"spread":false},{"title":"plotc.m <span style='color:#111;'> 3.17KB </span>","children":null,"spread":false},{"title":"cemd_visu.m <span style='color:#111;'> 2.76KB </span>","children":null,"spread":false},{"title":"cenvelope.m <span style='color:#111;'> 2.00KB </span>","children":null,"spread":false},{"title":"boundary_conditions_emd.m <span style='color:#111;'> 3.46KB </span>","children":null,"spread":false},{"title":"rmtag.m <span style='color:#111;'> 820B </span>","children":null,"spread":false}],"spread":false},{"title":"examples","children":[{"title":"NSIP2003","children":[{"title":"ex_online.m <span style='color:#111;'> 2.02KB </span>","children":null,"spread":false},{"title":"emd_fmsin.m <span style='color:#111;'> 1.82KB </span>","children":null,"spread":false},{"title":"emd_triang.m <span style='color:#111;'> 704B </span>","children":null,"spread":false},{"title":"emd_separation.m <span style='color:#111;'> 1.30KB </span>","children":null,"spread":false},{"title":"triangular_signal.m <span style='color:#111;'> 378B </span>","children":null,"spread":false},{"title":"emd_sampling.m <span style='color:#111;'> 896B </span>","children":null,"spread":false}],"spread":true},{"title":"SPL2007","children":[{"title":"float_position_record.mat <span style='color:#111;'> 8.34KB </span>","children":null,"spread":false},{"title":"dirstretch.m <span style='color:#111;'> 614B </span>","children":null,"spread":false},{"title":"bivariate_EMD_illustration.m <span style='color:#111;'> 1.25KB </span>","children":null,"spread":false},{"title":"bivariate_EMD_principle.m <span style='color:#111;'> 3.84KB </span>","children":null,"spread":false},{"title":"bivariate_EMD_mean_definitions.m <span style='color:#111;'> 2.91KB </span>","children":null,"spread":false}],"spread":true}],"spread":true},{"title":"uninstall_emd.m <span style='color:#111;'> 2.08KB </span>","children":null,"spread":false},{"title":"revert_bugfix.sh <span style='color:#111;'> 216B </span>","children":null,"spread":false},{"title":"index_emd.m <span style='color:#111;'> 4.38KB </span>","children":null,"spread":false}],"spread":true}],"spread":true}]

评论信息

  • yonghuxjp :
    挺好的,很有用
    2015-11-29
  • ljh419 :
    大致看了下,程序做的挺全面,现在还不理解原理
    2015-10-25
  • xiaohaoP :
    还是很好的资源的,有用!
    2015-10-21
  • 爱问的学习者 :
    程序挺全面的,适合初学者,对理解算法有很大帮助,多谢。
    2015-05-30
  • mountzf :
    挺好的,结合希尔伯特变换加深理解
    2015-05-08

免责申明

【只为小站】的资源来自网友分享,仅供学习研究,请务必在下载后24小时内给予删除,不得用于其他任何用途,否则后果自负。基于互联网的特殊性,【只为小站】 无法对用户传输的作品、信息、内容的权属或合法性、合规性、真实性、科学性、完整权、有效性等进行实质审查;无论 【只为小站】 经营者是否已进行审查,用户均应自行承担因其传输的作品、信息、内容而可能或已经产生的侵权或权属纠纷等法律责任。
本站所有资源不代表本站的观点或立场,基于网友分享,根据中国法律《信息网络传播权保护条例》第二十二条之规定,若资源存在侵权或相关问题请联系本站客服人员,zhiweidada#qq.com,请把#换成@,本站将给予最大的支持与配合,做到及时反馈和处理。关于更多版权及免责申明参见 版权及免责申明