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固定时间梯度流在l1-l2范数中的稀疏重构

胡登洲 何兴

胡登洲, 何兴. 固定时间梯度流在l1-l2范数中的稀疏重构[J]. 应用数学和力学, 2019, 40(11): 1270-1277. doi: 10.21656/1000-0887.400202
引用本文: 胡登洲, 何兴. 固定时间梯度流在l1-l2范数中的稀疏重构[J]. 应用数学和力学, 2019, 40(11): 1270-1277. doi: 10.21656/1000-0887.400202
HU Dengzhou, HE Xing. Sparse Reconstruction of Fixed-Time Gradient Flow in the l1-l2 Norm[J]. Applied Mathematics and Mechanics, 2019, 40(11): 1270-1277. doi: 10.21656/1000-0887.400202
Citation: HU Dengzhou, HE Xing. Sparse Reconstruction of Fixed-Time Gradient Flow in the l1-l2 Norm[J]. Applied Mathematics and Mechanics, 2019, 40(11): 1270-1277. doi: 10.21656/1000-0887.400202

固定时间梯度流在l1-l2范数中的稀疏重构

doi: 10.21656/1000-0887.400202
基金项目: 国家自然科学基金(61773320)
详细信息
    作者简介:

    胡登洲(1992—),男,硕士生(E-mail: 1076060236@qq.com);何兴(1986—),男,教授,博士生导师(通讯作者. E-mail: hexingdoc@swu.edu.cn).

  • 中图分类号: O357.41

Sparse Reconstruction of Fixed-Time Gradient Flow in the l1-l2 Norm

Funds: The National Natural Science Foundation of China(61773320)
  • 摘要: 压缩感知(compressed sensing,CS)是一种全新的信号采样技术,对于稀疏信号,它能够以远小于传统的Nyquist采样定理的采样点来重构信号。在压缩感知中, 采用动态连续系统,对l1-l2范数的稀疏信号重构问题进行了研究。提出了一种基于固定时间梯度流的稀疏信号重构算法,证明了该算法在Lyapunov意义上的稳定性并且收敛于问题的最优解。最后通过与现有的投影神经网络算法的对比,体现了该算法的可行性以及在收敛速度上的优势.
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出版历程
  • 收稿日期:  2019-07-02
  • 修回日期:  2019-07-09
  • 刊出日期:  2019-11-01

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