According to the exact expression of the maladjustment, an equation for calculating the boundary of step-size in MLMS algorithm is derived and the relationship between the convergence rate and step-size is discussed i...According to the exact expression of the maladjustment, an equation for calculating the boundary of step-size in MLMS algorithm is derived and the relationship between the convergence rate and step-size is discussed in detail. It is shown that the threshold of the step-size is constrained by maladjustment. Three different properties are presented between the LMS and MLMS algorithms based on comparison. It is indicated that MLMS does not differ significantly from LMS when the given maladjustment is small.展开更多
针对传统奇异值阈值(Singular Value Thresholding,SVT)数据恢复算法在对电力负荷数据恢复中忽视数据先验信息以及大规模数据计算效率低等问题,提出一种基于相空间重构与自适应变步长的改进SVT的数据恢复算法.为解决传统SVT容易忽视数...针对传统奇异值阈值(Singular Value Thresholding,SVT)数据恢复算法在对电力负荷数据恢复中忽视数据先验信息以及大规模数据计算效率低等问题,提出一种基于相空间重构与自适应变步长的改进SVT的数据恢复算法.为解决传统SVT容易忽视数据先验信息的问题,引入相空间重构算法将原始缺失数据映射到高维空间,利用数据间的关联性和结构特征,为后续数据恢复算法提供先验知识;结合对数与Sigmoid函数构建变步长基础函数,并利用等比项提高前期步长,构建自适应变步长SVT算法,克服传统SVT在大规模数据情况下计算效率低的问题.结合多项公用电力负荷数据集及多种常用电力负荷数据恢复算法进行对比实验分析,结果表明,改进SVT算法可获得更好的数据恢复效果,收敛速度、精度以及稳定性得到提升,具有较强的工程实用性.展开更多
文摘According to the exact expression of the maladjustment, an equation for calculating the boundary of step-size in MLMS algorithm is derived and the relationship between the convergence rate and step-size is discussed in detail. It is shown that the threshold of the step-size is constrained by maladjustment. Three different properties are presented between the LMS and MLMS algorithms based on comparison. It is indicated that MLMS does not differ significantly from LMS when the given maladjustment is small.
文摘针对传统奇异值阈值(Singular Value Thresholding,SVT)数据恢复算法在对电力负荷数据恢复中忽视数据先验信息以及大规模数据计算效率低等问题,提出一种基于相空间重构与自适应变步长的改进SVT的数据恢复算法.为解决传统SVT容易忽视数据先验信息的问题,引入相空间重构算法将原始缺失数据映射到高维空间,利用数据间的关联性和结构特征,为后续数据恢复算法提供先验知识;结合对数与Sigmoid函数构建变步长基础函数,并利用等比项提高前期步长,构建自适应变步长SVT算法,克服传统SVT在大规模数据情况下计算效率低的问题.结合多项公用电力负荷数据集及多种常用电力负荷数据恢复算法进行对比实验分析,结果表明,改进SVT算法可获得更好的数据恢复效果,收敛速度、精度以及稳定性得到提升,具有较强的工程实用性.
