An inertial projection neural network for sparse signal reconstruction via l_(1-2) minimization

摘要

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. (c) 2018 Elsevier B.V. All rights reserved.