Loughborough University
Leicestershire, UK
LE11 3TU
+44 (0)1509 263171
Loughborough University

Loughborough University Institutional Repository

Please use this identifier to cite or link to this item: https://dspace.lboro.ac.uk/2134/5586

Title: Steady-state performance of incremental learning over distributed networks for non-Gaussian data.
Authors: Li, Leilei
Zhang, Yonggang
Chambers, Jonathon
Sayed, Ali H.
Keywords: Adaptive filters
Distributed estimation
Energy conservation
Incremental algorithm
Issue Date: 2008
Publisher: © IEEE
Citation: LI, L. ... et al., 2008. Steady-state performance of incremental learning over distributed networks for non-Gaussian data. IN: Proceedings of 2008 9th International Conference on Signal Processing (ICSP 2008), Beijing, China, 26-29 October, pp. 227-230.
Abstract: In this paper, the steady-state performance of the distributed least mean-squares (dLMS) algorithm within an incremental network is evaluated without the restriction of Gaussian distributed inputs. Computer simulations are presented to verify the derived performance expressions.
Description: This is a conference paper [© IEEE]. It is also available at: http://ieeexplore.ieee.org/ Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
Version: Published
DOI: 10.1109/ICOSP.2008.4697112
URI: https://dspace.lboro.ac.uk/2134/5586
Appears in Collections:Conference Papers and Contributions (Electronic, Electrical and Systems Engineering)

Files associated with this item:

File Description SizeFormat
Steady-State Performance of Incremental Learning.pdf448.67 kBAdobe PDFView/Open

 

SFX Query

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.