自整定多元变分模态分解  

Self-tuning Multivariate Variational Mode Decomposition

作　　者：郎恂 王佳艺 陈启明 何冰冰 毛汝凯 谢磊[2] LANG Xun;WANG Jiayi;CHEN Qiming;HE Bingbing;MAO Rukai;XIE Lei(School of Information Science and Engineering,Yunnan University,Kunming 650504,China;State Key Laboratory of Industrial Control Technology,Zhejiang University,Hangzhou 310027,China;Yunnan Yuntianhua Limited by Share Ltd.,Kunming 650000,China)

机构地区：[1]云南大学信息学院,昆明650504 [2]浙江大学工业控制技术国家重点实验室,杭州310027 [3]云南云天化股份有限公司装备技术中心,昆明650000

出　　处：《电子与信息学报》2024年第7期2994-3001,共8页Journal of Electronics & Information Technology

基　　金：国家自然科学基金(62003298,62201495);云南省基础研究计划(202301AT070277);云南省重大科技专项(202202AD080005,202202AH080009)。

摘　　要：多元变分模态分解(MVMD)作为变分模态分解(VMD)的多元扩展,在继承VMD优点的同时,也存在其分解性能很大程度上依赖于两个预置参数——模态数量K和惩罚系数α的问题。为此,该文提出一种自整定MVMD(SMVMD)算法。SMVMD采取了匹配追踪法的思想,通过频域的能量占比和模态正交性分别自适应地更新K和α。对仿真信号与真实案例的分析结果表明,所提SMVMD方法不仅有效解决了原MVMD的参数整定问题,而且表现出以下优势,(1)与MVMD相比,SMVMD抗模态混叠的能力更强,且对噪声和α值的变化都具有更好的鲁棒性。(2)与多元经验模态分解、快速多元经验模态分解和多元变分模态分解这些经典算法相比,SMVMD算法的分解误差最小,分解效果最好。The Multivariate Variational Mode Decomposition(MVMD),being an extension of the Variational Mode Decomposition(VMD),inherits the merits of VMD.However,it encounters an issue wherein its decomposition performance relies heavily on two predefined parameters,the number of modes(K)and the penalty factor(α).To address this issue,a Self-tuning MVMD(SMVMD)algorithm is proposed.SMVMD employs the notion of matching pursuit to adaptively update K andαbased on energy occupation and mode orthogonality in the frequency domain,respectively.The experimented results of both simulated signals and real cases demonstrate that the proposed SMVMD not only effectively addresses the parameter rectification problem of the original MVMD,but also exhibits the following advantages:(1)SMVMD displays superior resilience to mode-mixing compared to MVMD,along with enhanced robustness to both noise and variations inα-value.(2)In comparison to the classical algorithms of multivariate empirical mode decomposition,fast multivariate empirical mode decomposition,and multivariate variational mode decomposition,SMVMD showcases the lowest decomposition error and the best decomposition effect.

关 键 词：多元信号处理 MVMD 自整定 匹配追踪法 鲁棒性 

分 类 号：TN911.7[电子电信—通信与信息系统]