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Variable length adaptive filtering within incremental learning algorithms for distributed networks

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conference contribution
posted on 30.11.2009 by Leilei Li, Yonggang Zhang, Jonathon Chambers
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 tap-length solution to determine the length of the adaptive filter within each node, the coefficients of which are adapted with an incremental-learning 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.

History

School

  • Mechanical, Electrical and Manufacturing Engineering

Citation

LI, L., ZHANG, Y. and CHAMBERS, J., 2008. Variable length adaptive filtering within incremental learning algorithms for distributed networks. IN: 42nd Asilomar Conference on Signals, Systems and Computers, Pacific Grove, CA, 26-29 October, pp. 225-229

Publisher

© IEEE

Version

VoR (Version of Record)

Publication date

2008

Notes

This is a conference paper [© IEEE]. It is also available at: http://ieeexplore.ieee.org/ Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.

ISBN

9781424429400

ISSN

1058-6393

Language

en

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