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.

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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 :	Name Email ORCID Jafari, MG Chambers, JA j.a.chambers@surrey.ac.uk Mandic, DP
Date :	1 April 2004
DOI :	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

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