Journal
NEUROCOMPUTING
Volume 173, Issue -, Pages 988-993Publisher
ELSEVIER SCIENCE BV
DOI: 10.1016/j.neucom.2015.08.055
Keywords
Neural network; Scaled gradient projection; l(1) - l(2) minimization; Compressed sensing
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Funding
- Natural Science Foundation of Hainan Province of China [114006]
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Since compressed sensing was introduced in 2006, l(1) - l(2) minimization admits a large number of applications in signal processing, statistical inference, magnetic resonance imaging (Mm), computed tomography (CT), etc. In this paper, we present a neural network for l(1) - l(2) minimization based on scaled gradient projection. We prove that it is stable in the sense of Lyapunov and converges to an optimal solution of the l(1) - l(2) minimization. We show that the proposed neural network is feasible and efficient for compressed sensing via simulation examples. (C) 2015 Elsevier B.V. All rights reserved.
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