Radial basis function cascade network for Sparse signal Recovery (RASR)

Author

Vivekanand, V. ; Vidya, L. ; Kumar, U. Shyam ; Mishra, Debahuti

Author_Institution

Vikram Sarabhai Space Centre, ISRO, Thiruvananthapuram, India

fYear

2014

fDate

Feb. 28 2014-March 2 2014

Firstpage

1

Lastpage

5

Abstract

The use of cascade network consisting of RBF nodes and least square error minimization block to Compressed Sensing for recovery of sparse signals is explored in this paper to improve the computation time and convergence. The proposed algorithm Radial basis function cascade network for Sparse signal Recovery (RASR) uses the L₀ norm optimization, L₂ least square method and feedback network model to improve the signal recovery performance and computational time over the existing ANN based CSIANN and relaxation based SL0 algorithms. The simulation results and experimental evluation of algorithm performance are presented here.

Keywords

cascade networks; compressed sensing; least squares approximations; neural nets; radial basis function networks; CSIANN; RASR; compressed sensing; computational time; feedback network model; least square error minimization block; least square method; radial basis function cascade network; sparse signal recovery; Algorithm design and analysis; Approximation algorithms; Artificial neural networks; Compressed sensing; Convergence; Minimization; Sparse matrices;

fLanguage

English

Publisher

ieee

Conference_Titel

Communications (NCC), 2014 Twentieth National Conference on

Conference_Location

Kanpur

Type

conf

DOI

10.1109/NCC.2014.6811251

Filename

6811251