Sparse LMS for system identification
ICASSP '09 Proceedings of the 2009 IEEE International Conference on Acoustics, Speech and Signal Processing
Steady-state performance analysis for adaptive filters with error nonlinearities
ICASSP '09 Proceedings of the 2009 IEEE International Conference on Acoustics, Speech and Signal Processing
Journal of Signal Processing Systems
Exact performance analysis of the ε-NLMS algorithm for colored circular Gaussian inputs
IEEE Transactions on Signal Processing
Normalized data nonlinearities for LMS adaptation
IEEE Transactions on Signal Processing
On the convergence behavior of the LMS and the normalized LMSalgorithms
IEEE Transactions on Signal Processing
A unified approach to the steady-state and tracking analyses ofadaptive filters
IEEE Transactions on Signal Processing
Transient analysis of data-normalized adaptive filters
IEEE Transactions on Signal Processing
Transient analysis of adaptive filters with error nonlinearities
IEEE Transactions on Signal Processing
Convergence and performance analysis of the normalized LMS algorithm with uncorrelated Gaussian data
IEEE Transactions on Information Theory
Nonnegative Least-Mean-Square Algorithm
IEEE Transactions on Signal Processing
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The zero attracting normalized least mean square (ZA-NLMS) algorithm achieves lower steady-state error than the normalized least mean square (NLMS) algorithm for sparse system identification. Most of the available analytical results on several versions of the zero attracting least mean square algorithms assume white Gaussian inputs. This paper presents the individual weight error variance (IWV) analysis of the ZA-NLMS algorithm without Gaussian inputs assumption. The IWV analysis is based on exact individual weight error relation and used to derive the transient and steady-state behavior of the ZA-NLMS algorithm without restricting the input to being Gaussian or white, whereas some assumptions are introduced to overcome weight nonlinearity in evaluating certain expectations involved. Extensive simulations are used to verify the analysis results presented.