Regularized LAD algorithms for sparse time-varying system identification with outliers

Two regularized least mean absolute deviation (LAD) algorithms are proposed for sparse system identification, which are referred to as zero-attracting LAD (ZA-LAD) and re-weighted zero-attracting LAD (RZA-LAD), respectively. The LAD type algorithms are robust to the impulsive noises. Furthermore, ℓ...

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Main Authors: Fuxi Wen, Wei Liu
Format: Conference Proceeding
Language:English
Subjects:
Adaptive systems
Algorithm design and analysis
Convergence
Cost function
least mean absolute deviation
outliers
Prediction algorithms
sparsity
Steady-state
time-varying system identification
Time-varying systems
zero-attracting
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Wei Liu
description Two regularized least mean absolute deviation (LAD) algorithms are proposed for sparse system identification, which are referred to as zero-attracting LAD (ZA-LAD) and re-weighted zero-attracting LAD (RZA-LAD), respectively. The LAD type algorithms are robust to the impulsive noises. Furthermore, ℓ 1 -norm penalty is imposed on the filter coefficients to exploit sparsity of the system. The performance of ZA-LAD type algorithms is evaluated for linear time varying system identification under impulsive noise environments through computer simulations.
doi_str_mv 10.1109/ICDSP.2016.7868630
format conference_proceeding
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subjects Adaptive systems
Algorithm design and analysis
Convergence
Cost function
least mean absolute deviation
outliers
Prediction algorithms
sparsity
Steady-state
time-varying system identification
Time-varying systems
zero-attracting
title Regularized LAD algorithms for sparse time-varying system identification with outliers
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