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An inertial projection neural network for sparse signal reconstruction via [formula omitted] minimization.
- Source :
-
Neurocomputing . Nov2018, Vol. 315, p89-95. 7p. - Publication Year :
- 2018
-
Abstract
- Abstract In this paper, an inertial projection neural network (IPNN) is proposed for the reconstruction of sparse signals. Firstly, a nonconvex l 1 − 2 minimization problem is presented for sparse signal reconstruction from highly coherent measurement matrices, instead of our familiar l 1 minimization which used standard convex relaxation. For solving this nonconvex l 1 − 2 minimization problem, the IPNN is introduced. Under certain condition, the convergence of IPNN is proved. Finally, a series of experiments on various applications are conducted and experimental results show the effectiveness and performance of IPNN for the introduced l 1 − 2 minimization method. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 09252312
- Volume :
- 315
- Database :
- Academic Search Index
- Journal :
- Neurocomputing
- Publication Type :
- Academic Journal
- Accession number :
- 131689880
- Full Text :
- https://doi.org/10.1016/j.neucom.2018.06.050