Improved convergence performance of adaptive algorithms through logarithmic cost

Date
2014-05
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Source Title
IEEE International Conference on Acoustics, Speech and Signal Processing. Proceedings
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Publisher
IEEE
Volume
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Pages
4513 - 4517
Language
English
Type
Conference Paper
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Abstract

We present a novel family of adaptive filtering algorithms based on a relative logarithmic cost. The new family intrinsically combines the higher and lower order measures of the error into a single continuous update based on the error amount. We introduce the least mean logarithmic square (LMLS) algorithm that achieves comparable convergence performance with the least mean fourth (LMF) algorithm and overcomes the stability issues of the LMF algorithm. In addition, we introduce the least logarithmic absolute difference (LLAD) algorithm. The LLAD and least mean square (LMS) algorithms demonstrate similar convergence performance in impulse-free noise environments while the LLAD algorithm is robust against impulsive interference and outperforms the sign algorithm (SA). © 2014 IEEE.

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Keywords
Logarithmic cost function, Robustness against impulsive noise, Stable adaptive method, Adaptive algorithms, Absolute difference, Adaptive filtering algorithms, Adaptive methods, Convergence performance, Improved convergence, Impulsive interference, Least mean square algorithms, Noise environments, Signal processing
Citation
Published Version (Please cite this version)