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

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

doi: 10.21656/1000-0887.400202
Funds:  The National Natural Science Foundation of China(61773320)
  • Received Date: 2019-07-02
  • Rev Recd Date: 2019-07-09
  • Publish Date: 2019-11-01
  • The compressed sensing (CS) is a new signal sampling technology, which can reconstruct signals at sampling points far smaller than those in the traditional Nyquist sampling theorem for sparse signals. For the compressed sensing, a dynamic continuous system was used to study the sparse signal reconstruction of the l1l-l2 norm. A sparse signal reconstruction algorithm based on the fixed time gradient flow was proposed, and was proved to be stable in the sense of Lyapunov and to converge to the optimal solution of the problem. Finally, the feasibility and advantages in the convergence speed of this algorithm were demonstrated through comparison between the proposed algorithm and existing projection neural network algorithms.
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