陈庆堂,黄宜坚.VMD滤波重构的时间序列自回归建模研究[J].电子测量与仪器学报,2020,34(3):155-162
VMD滤波重构的时间序列自回归建模研究
Research of time series autoregressive modeling based on VMD filtering reconstruction
  
DOI:
中文关键词:  VMD  自回归模型  磁流变
英文关键词:variational mode decomposition  autoregressive model  magnetorheological
基金项目:国家自然科学基金(50975098)、福建省科技引导性配套(2018H0031P)资助项目
作者单位
陈庆堂 1.莆田学院 机电工程学院 
黄宜坚 2.华侨大学 机电及自动化学院 
AuthorInstitution
Chen Qingtang 1.College of Mechanical Engineering and Electrical, Putian University 
Huang Yijian 2.College of Mechanical Engineering and Automation, Huaqiao University 
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中文摘要:
      为了获取反映磁流变减振系统自身特征的分析信号,选取适合系统的时间序列自回归模型,提出了谐波系数搜索最优惩罚因子和分解模数的变分模态分解(VMD)滤波重构方法,通过建立与系统的动力学模型同阶的重构分析信号时间序列ARMA和AR模型,对比基于快速傅里叶变换(FFT)、经验模态分解(EMD)信号滤波重构算法,分析了各模型模拟精度。研究表明,3种滤波重构方法中, 未简化的高阶模型均比简化的低阶模型拟合精度高,同阶ARMA模型模拟精度均比AR模型高,采用谐波系数搜索最优惩罚因子和分解模数的VMD滤波重构方法的自回归模型模拟精度最高,其中基于VMD重构信号的ARMA(4,1)模型建模精度最高,最适合用于系统的建模与分析。
英文摘要:
      In order to obtain the analysis signals reflecting the characteristics of the system, find the signal reconstruction method and autoregressive model that suitable for time series of the system. VMD filtering reconstruction method using harmonic coefficients to search the optimal penalty factor and decompose modulus is proposed to filter and reconstruct analysis signals of MR damper system, then ARMA and AR models of the analysis signals of the same order as the dynamic model of the system are established, compared with the reconstruction algorithm based on FFT and EMD signal filtering, the simulation accuracy of these models are analyzed. The results show that, among the three filtering reconstruction methods, the fitting accuracy of the simplified low order model is lower than that of the non simplified high order model, the simulation accuracy of the same order ARMA model is higher than that of the AR model, VMD filtering reconstruction method using harmonic coefficients to search the optimal penalty factor and decompose modulus has the highest simulation accuracy of the autoregressive model. Among them, ARMA (4,1) model based on VMD reconstructed signal has the highest modeling accuracy and is most suitable for system modeling and analysis.
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