Infoscience

Conference paper

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.

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