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논문 기본정보
A neural network for @?1-@?2 minimization based on scaled gradient projection: Application to compressed sensing
논문 개요
| 기관명 | NDSL |
|---|---|
| 저널명 | Neurocomputing |
| ISSN | 0925-2312, |
| ISBN |
논문저자 및 소속기관 정보
| 저자(한글) | Liu, Y.,Hu, J. |
|---|---|
| 저자(영문) | |
| 소속기관 | |
| 소속기관(영문) | |
| 출판인 | |
| 간행물 번호 | |
| 발행연도 | 2016-01-01 |
| 초록 | Since compressed sensing was introduced in 2006, @? 1 -@? 2 minimization admits a large number of applications in signal processing, statistical inference, magnetic resonance imaging (MRI), computed tomography (CT), etc. In this paper, we present a neural network for @? 1 -@? 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 @? 1 -@? 2 minimization. We show that the proposed neural network is feasible and efficient for compressed sensing via simulation examples. |
| 원문URL | http://click.ndsl.kr/servlet/OpenAPIDetailView?keyValue=03553784&target=NART&cn=NART73796629 |
| 첨부파일 |
추가정보
| 과학기술표준분류 | |
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| ICT 기술분류 | |
| DDC 분류 | |
| 주제어 (키워드) | Neural network,Scaled gradient projection,@? lt,SUB gt,1 lt,/SUB gt,-@? lt,SUB gt,2 lt,/SUB gt,minimization,Compressed sensing |