![](/images/graphics-bg.png)
Neural Network for Sparse Reconstruction
Joint Authors
Zhu, Liangkuan
Liu, Yaqiu
Li, Qingfa
Source
Mathematical Problems in Engineering
Issue
Vol. 2014, Issue 2014 (31 Dec. 2014), pp.1-8, 8 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2014-03-31
Country of Publication
Egypt
No. of Pages
8
Main Subjects
Abstract EN
We construct a neural network based on smoothing approximation techniques and projected gradient method to solve a kind of sparse reconstruction problems.
Neural network can be implemented by circuits and can be seen as an important method for solving optimization problems, especially large scale problems.
Smoothing approximation is an efficient technique for solving nonsmooth optimization problems.
We combine these two techniques to overcome the difficulties of the choices of the step size in discrete algorithms and the item in the set-valued map of differential inclusion.
In theory, the proposed network can converge to the optimal solution set of the given problem.
Furthermore, some numerical experiments show the effectiveness of the proposed network in this paper.
American Psychological Association (APA)
Li, Qingfa& Liu, Yaqiu& Zhu, Liangkuan. 2014. Neural Network for Sparse Reconstruction. Mathematical Problems in Engineering،Vol. 2014, no. 2014, pp.1-8.
https://search.emarefa.net/detail/BIM-446992
Modern Language Association (MLA)
Li, Qingfa…[et al.]. Neural Network for Sparse Reconstruction. Mathematical Problems in Engineering No. 2014 (2014), pp.1-8.
https://search.emarefa.net/detail/BIM-446992
American Medical Association (AMA)
Li, Qingfa& Liu, Yaqiu& Zhu, Liangkuan. Neural Network for Sparse Reconstruction. Mathematical Problems in Engineering. 2014. Vol. 2014, no. 2014, pp.1-8.
https://search.emarefa.net/detail/BIM-446992
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-446992