Sparse signal reconstruction via collaborative neurodynamic optimization

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

3 Scopus Citations
View graph of relations

Author(s)

Detail(s)

Original languageEnglish
Pages (from-to)255-269
Journal / PublicationNeural Networks
Volume154
Online published19 Jul 2022
Publication statusPublished - Oct 2022

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.

Research Area(s)

  • Collaborative neurodynamic optimization, q-ratio surrogate function, Sparse signal reconstruction, Sparsity maximization

Citation Format(s)

Sparse signal reconstruction via collaborative neurodynamic optimization. / Che, Hangjun; Wang, Jun; Cichocki, Andrzej.

In: Neural Networks, Vol. 154, 10.2022, p. 255-269.

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review