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

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.


Published in:
9th International Conference on Signal Processing, 227-230
Presented at:
9th International Conference on Signal Processing (ICSP 2008), Beijing, China, October 26-29, 2008
Year:
2008
Publisher:
IEEE
Laboratories:




 Record created 2017-12-19, last modified 2018-09-13


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