University of Surrey

Test tubes in the lab Research in the ATI Dance Research

A novel adaptive learning rate sequential blind source separation algorithm

Jafari, MG, Chambers, JA and Mandic, DP (2004) A novel adaptive learning rate sequential blind source separation algorithm Signal Processing, 84 (4). pp. 801-804.

Full text not available from this repository.

Abstract

A new member of the family of natural gradient algorithms for on-line blind separation of independent sources is proposed. The method is based upon an adaptive step-size which varies in sympathy with the dynamics of the input signals and properties of the de-mixing matrix, and is robust to the perturbations in the initial value of the learning rate parameter. As a result, the convergence speed is significantly improved, especially in non-stationary mixing environments. Simulations support the expected improvement in convergence speed of the approach. © 2003 Elsevier B.V. All rights reserved.

Item Type: Article
Authors :
NameEmailORCID
Jafari, MGUNSPECIFIEDUNSPECIFIED
Chambers, JAj.a.chambers@surrey.ac.ukUNSPECIFIED
Mandic, DPUNSPECIFIEDUNSPECIFIED
Date : 1 April 2004
Identification Number : https://doi.org/10.1016/j.sigpro.2003.12.012
Depositing User : Symplectic Elements
Date Deposited : 17 May 2017 13:27
Last Modified : 17 May 2017 13:27
URI: http://epubs.surrey.ac.uk/id/eprint/839258

Actions (login required)

View Item View Item

Downloads

Downloads per month over past year


Information about this web site

© The University of Surrey, Guildford, Surrey, GU2 7XH, United Kingdom.
+44 (0)1483 300800