Steady-state performance of incremental learning over distributed networks for non-Gaussian data
Li, Leilei; Zhang, Yonggang; Chambers, Jonathon A.; Sayed, Ali H.
2008

Abstract

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.

Details

Title Steady-state performance of incremental learning over distributed networks for non-Gaussian data
Author(s) Li, Leilei ; Zhang, Yonggang ; Chambers, Jonathon A. ; Sayed, Ali H.
Published in 9th International Conference on Signal Processing
Pages 227-230
Conference 9th International Conference on Signal Processing (ICSP 2008), Beijing, China, October 26-29, 2008
Date 2008
Publisher IEEE
DOI https://doi.org/10.1109/ICOSP.2008.4697112
Laboratories ASL
Record Appears in Scientific production and competences > STI - School of Engineering > IEM - Institut d'Electricité et de Microtechnique > ASL - Adaptative Systems Laboratory
Peer-reviewed publications
Work outside EPFL
Conference Papers
Published
Record creation date 2017-12-19

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