Variable length adaptive filtering within incremental learning algorithms for distributed networks

Leilei Li; Yonggang Zhang; Jonathon A. Chambers

doi:10.1109/ACSSC.2008.5074397

Variable length adaptive filtering within incremental learning algorithms for distributed networks

Leilei Li^*, Yonggang Zhang, Jonathon A. Chambers

^*Corresponding author for this work

Loughborough University

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

10 Citations (Scopus)

Abstract

In this paper we propose the use of variable length adaptive filtering within the context of incremental learning for distributed networks. Algorithms for such incremental learning strategies must have low computational complexity and require minimal communication between nodes as compared to centralized networks. To match the dynamics of the data across the network we optimize the length of the adaptive filters used within each node by exploiting the statistics of the local signals to each node. In particular, we use a fractional taplength solution to determine the length of the adaptive filter within each node, the coefficients of which are adapted with an incrementallearning learning algorithm. Simulation studies are presented to confirm the convergence properties of the scheme and these are verified by theoretical analysis of excess mean square error and mean square deviation.

Original language	English
Title of host publication	2008 42nd Asilomar Conference on Signals, Systems and Computers, ASILOMAR 2008
Pages	225-229
Number of pages	5
DOIs	https://doi.org/10.1109/ACSSC.2008.5074397
Publication status	Published - 2008
Externally published	Yes
Event	2008 42nd Asilomar Conference on Signals, Systems and Computers, ASILOMAR 2008 - Pacific Grove, CA, United States Duration: 26 Oct 2008 → 29 Oct 2008

Publication series

Name	Conference Record - Asilomar Conference on Signals, Systems and Computers
ISSN (Print)	1058-6393

Conference

Conference	2008 42nd Asilomar Conference on Signals, Systems and Computers, ASILOMAR 2008
Country/Territory	United States
City	Pacific Grove, CA
Period	26/10/08 → 29/10/08

Keywords

Adaptive filters
Distributed processing
Incremental algorithm
Variable tap-length

Access to Document

10.1109/ACSSC.2008.5074397

Cite this

Li, L., Zhang, Y., & Chambers, J. A. (2008). Variable length adaptive filtering within incremental learning algorithms for distributed networks. In 2008 42nd Asilomar Conference on Signals, Systems and Computers, ASILOMAR 2008 (pp. 225-229). Article 5074397 (Conference Record - Asilomar Conference on Signals, Systems and Computers). https://doi.org/10.1109/ACSSC.2008.5074397

@inproceedings{380f7350035940d890b702854c6f701c,

title = "Variable length adaptive filtering within incremental learning algorithms for distributed networks",

abstract = "In this paper we propose the use of variable length adaptive filtering within the context of incremental learning for distributed networks. Algorithms for such incremental learning strategies must have low computational complexity and require minimal communication between nodes as compared to centralized networks. To match the dynamics of the data across the network we optimize the length of the adaptive filters used within each node by exploiting the statistics of the local signals to each node. In particular, we use a fractional taplength solution to determine the length of the adaptive filter within each node, the coefficients of which are adapted with an incrementallearning learning algorithm. Simulation studies are presented to confirm the convergence properties of the scheme and these are verified by theoretical analysis of excess mean square error and mean square deviation.",

keywords = "Adaptive filters, Distributed processing, Incremental algorithm, Variable tap-length",

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

year = "2008",

doi = "10.1109/ACSSC.2008.5074397",

language = "English",

isbn = "9781424429417",

series = "Conference Record - Asilomar Conference on Signals, Systems and Computers",

pages = "225--229",

booktitle = "2008 42nd Asilomar Conference on Signals, Systems and Computers, ASILOMAR 2008",

note = "2008 42nd Asilomar Conference on Signals, Systems and Computers, ASILOMAR 2008 ; Conference date: 26-10-2008 Through 29-10-2008",

}

Li, L, Zhang, Y & Chambers, JA 2008, Variable length adaptive filtering within incremental learning algorithms for distributed networks. in 2008 42nd Asilomar Conference on Signals, Systems and Computers, ASILOMAR 2008., 5074397, Conference Record - Asilomar Conference on Signals, Systems and Computers, pp. 225-229, 2008 42nd Asilomar Conference on Signals, Systems and Computers, ASILOMAR 2008, Pacific Grove, CA, United States, 26/10/08. https://doi.org/10.1109/ACSSC.2008.5074397

Variable length adaptive filtering within incremental learning algorithms for distributed networks. / Li, Leilei; Zhang, Yonggang; Chambers, Jonathon A.
2008 42nd Asilomar Conference on Signals, Systems and Computers, ASILOMAR 2008. 2008. p. 225-229 5074397 (Conference Record - Asilomar Conference on Signals, Systems and Computers).

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

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AU - Zhang, Yonggang

AU - Chambers, Jonathon A.

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N2 - In this paper we propose the use of variable length adaptive filtering within the context of incremental learning for distributed networks. Algorithms for such incremental learning strategies must have low computational complexity and require minimal communication between nodes as compared to centralized networks. To match the dynamics of the data across the network we optimize the length of the adaptive filters used within each node by exploiting the statistics of the local signals to each node. In particular, we use a fractional taplength solution to determine the length of the adaptive filter within each node, the coefficients of which are adapted with an incrementallearning learning algorithm. Simulation studies are presented to confirm the convergence properties of the scheme and these are verified by theoretical analysis of excess mean square error and mean square deviation.

AB - In this paper we propose the use of variable length adaptive filtering within the context of incremental learning for distributed networks. Algorithms for such incremental learning strategies must have low computational complexity and require minimal communication between nodes as compared to centralized networks. To match the dynamics of the data across the network we optimize the length of the adaptive filters used within each node by exploiting the statistics of the local signals to each node. In particular, we use a fractional taplength solution to determine the length of the adaptive filter within each node, the coefficients of which are adapted with an incrementallearning learning algorithm. Simulation studies are presented to confirm the convergence properties of the scheme and these are verified by theoretical analysis of excess mean square error and mean square deviation.

KW - Adaptive filters

KW - Distributed processing

KW - Incremental algorithm

KW - Variable tap-length

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U2 - 10.1109/ACSSC.2008.5074397

DO - 10.1109/ACSSC.2008.5074397

M3 - Conference contribution

AN - SCOPUS:70349685023

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T2 - 2008 42nd Asilomar Conference on Signals, Systems and Computers, ASILOMAR 2008

Y2 - 26 October 2008 through 29 October 2008

ER -

Variable length adaptive filtering within incremental learning algorithms for distributed networks

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