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Convergence and steady-state properties of the least-mean mixed-norm (LMMN) adaptive algorithm

Tanrikulu, O and Chambers, JA (1996) Convergence and steady-state properties of the least-mean mixed-norm (LMMN) adaptive algorithm IEE Proceedings: Vision, Image and Signal Processing, 143 (3). pp. 137-142.

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Abstract

Convergence and steady-state analyses of a least-mean mixed-norm adaptive algorithm are presented. This is formed as a convex mixture of the mean-square and the mean-fourth cost functions. The local exponential stability of the algorithm is shown by application of the deterministic averaging analysis and the total stability theorem. A theoretical misadjustment expression is then obtained by using the ordinary-differential-equation method. Simulation studies are presented to support the theoretical findings. The results demonstrate the advantage of mixing error norms in adaptive filtering when the measurement noise is composed of a linear combination of long-tail and short-tail noise distributions. © IEE, 1996.

Item Type: Article
Authors :
NameEmailORCID
Tanrikulu, OUNSPECIFIEDUNSPECIFIED
Chambers, JAj.a.chambers@surrey.ac.ukUNSPECIFIED
Date : 1 December 1996
Depositing User : Symplectic Elements
Date Deposited : 17 May 2017 13:27
Last Modified : 17 May 2017 13:27
URI: http://epubs.surrey.ac.uk/id/eprint/839295

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