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

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

doi:10.1109/ICOSP.2008.4697112

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

Leilei Li^*, Yonggang Zhang, Jonathon A. Chambers, Ali H. Sayed

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Citation (Scopus)

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.

Original language	English
Title of host publication	2008 9th International Conference on Signal Processing, ICSP 2008
Pages	227-230
Number of pages	4
DOIs	https://doi.org/10.1109/ICOSP.2008.4697112
Publication status	Published - 2008
Externally published	Yes
Event	2008 9th International Conference on Signal Processing, ICSP 2008 - Beijing, China Duration: 26 Oct 2008 → 29 Oct 2008

Publication series

Name	International Conference on Signal Processing Proceedings, ICSP

Conference

Conference	2008 9th International Conference on Signal Processing, ICSP 2008
Country/Territory	China
City	Beijing
Period	26/10/08 → 29/10/08

Keywords

Adaptive filters
Distributed estimation
Energy conservation
Incremental algorithm

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1109/ICOSP.2008.4697112

Cite this

@inproceedings{ff9a8b920709427b856ddbed04cb1d44,

title = "Steady-state performance of incremental learning over distributed networks for non-gaussian data",

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.",

keywords = "Adaptive filters, Distributed estimation, Energy conservation, Incremental algorithm",

author = "Leilei Li and Yonggang Zhang and Chambers, \{Jonathon A.\} and Sayed, \{Ali H.\}",

year = "2008",

doi = "10.1109/ICOSP.2008.4697112",

language = "English",

isbn = "9781424421794",

series = "International Conference on Signal Processing Proceedings, ICSP",

pages = "227--230",

booktitle = "2008 9th International Conference on Signal Processing, ICSP 2008",

note = "2008 9th International Conference on Signal Processing, ICSP 2008 ; Conference date: 26-10-2008 Through 29-10-2008",

}

Li, L, Zhang, Y, Chambers, JA & Sayed, AH 2008, Steady-state performance of incremental learning over distributed networks for non-gaussian data. in 2008 9th International Conference on Signal Processing, ICSP 2008., 4697112, International Conference on Signal Processing Proceedings, ICSP, pp. 227-230, 2008 9th International Conference on Signal Processing, ICSP 2008, Beijing, China, 26/10/08. https://doi.org/10.1109/ICOSP.2008.4697112

Steady-state performance of incremental learning over distributed networks for non-gaussian data. / Li, Leilei; Zhang, Yonggang; Chambers, Jonathon A. et al.
2008 9th International Conference on Signal Processing, ICSP 2008. 2008. p. 227-230 4697112 (International Conference on Signal Processing Proceedings, ICSP).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Steady-state performance of incremental learning over distributed networks for non-gaussian data

AU - Li, Leilei

AU - Zhang, Yonggang

AU - Chambers, Jonathon A.

AU - Sayed, Ali H.

PY - 2008

Y1 - 2008

N2 - 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.

AB - 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.

KW - Adaptive filters

KW - Distributed estimation

KW - Energy conservation

KW - Incremental algorithm

UR - https://www.scopus.com/pages/publications/67249153611

U2 - 10.1109/ICOSP.2008.4697112

DO - 10.1109/ICOSP.2008.4697112

M3 - Conference contribution

AN - SCOPUS:67249153611

SN - 9781424421794

T3 - International Conference on Signal Processing Proceedings, ICSP

SP - 227

EP - 230

BT - 2008 9th International Conference on Signal Processing, ICSP 2008

T2 - 2008 9th International Conference on Signal Processing, ICSP 2008

Y2 - 26 October 2008 through 29 October 2008

ER -

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

Abstract

Publication series

Conference

Keywords

UN SDGs

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