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Steady-state performance of incremental learning over distributed networks for non-Gaussian data.
conference contribution
posted on 2009-12-04, 08:56 authored by Leilei Li, Yonggang Zhang, Jonathon Chambers, Ali H. SayedIn 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.
History
School
- Mechanical, Electrical and Manufacturing Engineering
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.Publisher
© IEEEVersion
- VoR (Version of Record)
Publication date
2008Notes
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.Language
- en
