Journal ArticleDOI
Transient and steady-state MSE analysis of the IMPNLMS algorithm
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TLDR
An accurate transient analysis of the improved μ-law proportionate normalized least mean squares (IMPNLMS) algorithm is presented and an estimate of its steady-state MSE is derived, without requiring the assumption of white Gaussian input signals.About:
This article is published in Digital Signal Processing.The article was published on 2014-10-01. It has received 25 citations till now. The article focuses on the topics: Recursive least squares filter & Adaptive filter.read more
Citations
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Journal ArticleDOI
Steady-state mean-square-deviation analysis of the sign subband adaptive filter algorithm
Yi Yu,Haiquan Zhao,Badong Chen +2 more
TL;DR: The steady-state mean-square-deviation behavior of the SSAF algorithm is studied by using energy conservation relation, Price's theorem and some reasonable assumptions to support the theoretical analysis.
Journal ArticleDOI
Robust proportionate adaptive filter based on maximum correntropy criterion for sparse system identification in impulsive noise environments
TL;DR: Simulation results in sparse system identification and echo cancellation applications are presented, which demonstrate that the proposed proportionate MCC exhibits outstanding performance under the impulsive noise environments.
Journal ArticleDOI
Sparsity-aware SSAF algorithm with individual weighting factors: Performance analysis and improvements in acoustic echo cancellation
TL;DR: Compared with the existing analysis on the IWF-SSAF algorithm, the proposed analysis does not require the assumptions of large number of subbands, long adaptive filter, and paraunitary analysis filter bank, and matches well the simulated results.
Journal ArticleDOI
Transient analysis of l0-LMS and l0-NLMS algorithms
TL;DR: A stochastic model for both l0-LMS and l 0-NLMS algorithms is proposed, and an accurate transient analysis of these algorithms without requiring the input signal to be white is carried out.
Journal ArticleDOI
Combined regularization parameter for normalized LMS algorithm and its performance analysis
TL;DR: The proposed algorithm adaptively combines two different regularization parameters by employing a time-varying mixing parameter that is derived by minimizing the energy of noise-free a posteriori error to avoid large fluctuations.
References
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Book
Adaptive Filter Theory
TL;DR: In this paper, the authors propose a recursive least square adaptive filter (RLF) based on the Kalman filter, which is used as the unifying base for RLS Filters.
Book
Introduction to Numerical Analysis
Josef Stoer,Roland Bulirsch +1 more
TL;DR: This well written book is enlarged by the following topics: B-splines and their computation, elimination methods for large sparse systems of linear equations, Lanczos algorithm for eigenvalue problems, implicit shift techniques for theLR and QR algorithm, implicit differential equations, differential algebraic systems, new methods for stiff differential equations and preconditioning techniques.
Book
Adaptive Filters
TL;DR: Adaptive Filters offers a fresh, focused look at the subject in a manner that will entice students, challenge experts, and appeal to practitioners and instructors.
Book
Adaptive Filtering: Algorithms and Practical Implementation
TL;DR: Adaptive Filtering: Algorithms and Practical Implementation may be used as the principle text for courses on the subject, and serves as an excellent reference for professional engineers and researchers in the field.