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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 Presentations (Mechanical, Electrical and Manufacturing Engineering)

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