Steady-state performance of incremental learning over distributed networks for non-Gaussian data.

Li, Leilei; Zhang, Yonggang; Chambers, Jonathon; Sayed, Ali H.

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Steady-state performance of incremental learning over distributed networks for non-Gaussian data.

conference contribution

posted on 04.12.2009, 08:56 authored by Leilei Li, Yonggang Zhang, Jonathon Chambers, Ali H. Sayed

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.

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

Version

VoR (Version of Record)

Publication date

2008

Notes

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