Journal
NEURAL NETWORKS
Volume 99, Issue -, Pages 31-41Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.neunet.2017.12.008
Keywords
Sparse signal recovery; Lp-q minimization; Smoothing inertial projection neural network (SIPNN); Restricted isometry property (RIP) condition
Funding
- Natural Science Foundation of China [61403313, 61773320]
- Fundamental Research Funds for the Central Universities [XDJK2017D179, XDJK2016B017]
- China Postdoctoral Science Foundation [2016M600144]
- Research Foundation of Key laboratory of Machine Perception
- Children's Intelligence Development - CQUE, China [16xjpt07]
- NPRP from the Qatar National Research Fund (a member of Qatar Foundation) [NPRP 9-166-1-031]
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In this paper, we investigate a more general sparse signal recovery minimization model and a smoothing neural network optimal method for compress sensing problem, where the objective function is a Lp-q minimization model which includes nonsmooth, nonconvex, and non-Lipschitz quasi-norm L-p norms (1 >= p > 0) and nonsmooth L-q norms (2 >= p > 1), and its feasible set is a closed convex subset of R-n. Firstly, under the restricted isometry property (RIP) condition, the uniqueness of solution for the minimization model with a given sparsity s is obtained through the theoretical analysis. With a mild condition, we get that the larger of the q, the more effective of the sparse recovery model under sensing matrix satisfies RIP conditions at fixed p. Secondly, using a smoothing approximate method, we propose the smoothing inertial projection neural network (SIPNN) algorithm for solving the proposed general model. Under certain conditions, the proposed algorithm can converge to a stationary point. Finally, convergence behavior and successful recover performance experiments and a comparison experiment confirm the effectiveness of the proposed SIPNN algorithm. (c) 2017 Elsevier Ltd. All rights reserved.
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