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

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conference contribution
posted on 04.12.2009, 08:56 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.

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  • 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.

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© IEEE

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2008

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