Article ID Journal Published Year Pages File Type
846976 Optik - International Journal for Light and Electron Optics 2016 5 Pages PDF
Abstract

In this paper, neural network approach is addressed for signal reconstruction under the frame of compressed sensing. By introducing implicit variables, we convert the basis pursuit denoising model into a quadratic programming problem. Based on a class of generalized Fischer–Burmeister complementarity functions, we establish a neural network method for the signal reconstruction of compressed sensing. A projection neural network is also presented to recover the original signals. These two neural networks can be implemented using integrated circuits and two block diagrams of the neural networks are presented. Based on our proposed method, some potential applications of the compressed sensing are discussed.

Related Topics
Physical Sciences and Engineering Engineering Engineering (General)
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