Sparse signal reconstruction via collaborative neurodynamic optimization. (October 2022)

• Record Type: Journal Article
• Authors: Che, Hangjun; Wang, Jun; Cichocki, Andrzej
• Is Part Of: Neural networks. Volume 154(2022)
• Page Start: 255
• Page End: 269
• Publication Date: 2022-10
• Subjects: Sparse signal reconstruction -- Sparsity maximization -- q-ratio surrogate function -- Collaborative neurodynamic optimization; Neural computers -- Periodicals; Neural networks (Computer science) -- Periodicals; Neural networks (Neurobiology) -- Periodicals; Nervous System -- Periodicals; Ordinateurs neuronaux -- Périodiques; Réseaux neuronaux (Informatique) -- Périodiques; Réseaux neuronaux (Neurobiologie) -- Périodiques; Neural computers; Neural networks (Computer science); Neural networks (Neurobiology); Periodicals; 006.32
• DOI: 10.1016/j.neunet.2022.07.018
• Languages: English
• ISSNs: 0893-6080

Abstract

Abstract: In this paper, we formulate a mixed-integer problem for sparse signal reconstruction and reformulate it as a global optimization problem with a surrogate objective function subject to underdetermined linear equations. We propose a sparse signal reconstruction method based on collaborative neurodynamic optimization with multiple recurrent neural networks for scattered searches and a particle swarm optimization rule for repeated repositioning. We elaborate on experimental results to demonstrate the outperformance of the proposed approach against ten state-of-the-art algorithms for sparse signal reconstruction.