Proceedings ArticleDOI
A fast quasi-Newton adaptive filtering algorithm
D.F. Marshal,W.K. Jenkins +1 more
- pp 1377-1380
TLDR
Simulation results are presented which demonstrate that the proposed adaptive filtering algorithm's performance is comparable to RLS, and that it is quite robust with respect to finite-wordlength implementation.Abstract:
An adaptive filtering algorithm is introduced which is largely immune to the deleterious effects of colored inputs, yet requires only O(N) computation. Simulation results are presented which demonstrate that the proposed algorithm's performance is comparable to RLS (recursive least squares), and that it is quite robust with respect to finite-wordlength implementation. >read more
Citations
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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.
Journal ArticleDOI
Hammerstein uniform cubic spline adaptive filters: Learning and convergence properties
TL;DR: A novel class of nonlinear Hammerstein adaptive filters, consisting of a flexible memory-less function followed by a linear combiner, is presented, used for the identification of Hammerstein-type nonlinear systems.
Journal ArticleDOI
Fast LMS/Newton algorithms based on autoregressive modeling and their application to acoustic echo cancellation
TL;DR: Experiments with speech signals for modeling the acoustics of an office room show the superior convergence of the proposed algorithms when compared with the normalized LMS algorithm.
Journal ArticleDOI
Bias-remedy least mean square equation error algorithm for IIR parameter recursive estimation
J.-N. Lin,R. Unbehauen +1 more
TL;DR: A new algorithm is proposed on the basis of the least mean square equation error (LMSEE) algorithm, which manages to remedy the bias while retaining the parameter stability in the IIR system identification and adaptive filtering.
Journal ArticleDOI
Fast Affine Projection Adaptation Algorithms With Stable and Robust Symmetric Linear System Slovers
TL;DR: Two noniterative approaches to solve a symmetric linear system associated with the fast affine projection adaptation algorithm, using matrix LDLT factorization and a reciprocating recursion scheme are proposed.
References
More filters
Book
Adaptive filtering prediction and control
Graham C. Goodwin,Kwai Sang Sin +1 more
TL;DR: This unified survey focuses on linear discrete-time systems and explores the natural extensions to nonlinear systems and summarizes the theoretical and practical aspects of a large class of adaptive algorithms.
Journal ArticleDOI
Fast, recursive-least-squares transversal filters for adaptive filtering
John M. Cioffi,Thomas Kailath +1 more
TL;DR: Fast transversal filter (FTF) implementations of recursive-least-squares (RLS) adaptive-filtering algorithms are presented in this paper and substantial improvements in transient behavior in comparison to stochastic-gradient or LMS adaptive algorithms are efficiently achieved by the presented algorithms.
Book
Least Square Estimation with Applications to Digital Signal Processing
Arthur A. Giordano,Frank M. Hsu +1 more
TL;DR: A unified treatment of least squares based on geometric principles establishes the mathematical framework of least square estimation, demonstrating the utility and widespread use of these principles in a variety of digital signal processing applications.
Journal ArticleDOI
Self-Orthogonalizing Adaptive Equalization Algorithms
TL;DR: A comparison is made of several self-orthogonalizing adjustment algorithms for linear tapped delay line equalizers that accelerate the rate of convergence of the equalizer tap weights to those which minimize the output mean-squared error of a data transmission system.
Journal ArticleDOI
Fast algorithms for discrete-time Wiener filters with optimum lag
TL;DR: Two new algorithms for the efficient computation of the optimum lag Wiener filter as well as the optimum prediction distance predictor are introduced, featuring a coupled step-down step-up recursion which takes full advantage of both the Toeplitz nature of the matrix and the shifted structure of the right-hand side vectors.
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