Fundamentals of Adaptive Filtering

By P. S. Ramirez. 2002

1,319 citations recorded by the source corpus.

Subfields: Computational Mechanics, Signal Processing

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Citations in the corpus

One-Pass Learning via Bridging Orthogonal Gradient Descent and Recursive Least-Squares (2022, neutral): “Note that the system is underdetermined/overparameterized, and the regularization term in (1) is necessary for the solution to be uniquely deﬁned.”
Generalized Correntropy for Robust Adaptive Filtering (2015, methodology): “The well-known least mean square (LMS) algorithm and its variants, such as normalized LMS (NLMS) and variable step-size LMS (VSSLMS), were developed under this criterion [1], [2].”
Blockage and directivity in 60 GHz wireless personal area networks: from cross-layer model to multihop MAC design (2009, methodology): “A receiving antenna array uses a training sequence in the PHY preamble of a packet to adjust the required array weights in order to adaptively beamform towards the direction of the signal of interest [41], using standard adaptive algorithms [42], such as least mean squares (LMS) or recursive least s”
Complex-Valued Matrix Differentiation: Techniques and Key Results (2007, neutral): “[ 7 ] L. Gammaitoni, P. Hanggi, P. Jung, and F. Marchesoni, “Stochastic”
The Adaptive Projected Subgradient Method over the Fixed Point Set of Strongly Attracting Nonexpansive Mappings (2006, methodology): “However, the APA shows sensitivity for big r and under the assumption of nonstationary input data in environments corrupted severely by additive white Gaussian noise ([39]).”
Mean-square performance of a convex combination of two adaptive filters (2006, methodology): “In this paper, we pursue a detailed mean-square performance analysis of this scheme for both stationary and nonstationary environments by using energy conservation arguments [19].”
Compensation schemes and performance analysis of IQ imbalances in OFDM receivers (2005, methodology): “Now, the equalization coefﬁcients for are updated according to the LMS rules: Although LMS is the simplest adaptive implementation in terms of complexity, it suffers from a slow convergence rate [19].”
Diffusion recursive least-squares for distributed estimation over adaptive networks (2008, methodology): “For the EMSE, we get These are known results for the MSD and EMSE of regular RLS with close to 1 [11].”
Diffusion LMS Strategies for Distributed Estimation (2010, methodology): “To do so, we resort to the energy conservation analysis of [16], [17], [23], [31].”
Collaborative wideband sensing for cognitive radios (2008, methodology): “Since 0,k 0, the Rayleigh Ritz inequality [38] gives”
Cloud computing service composition: A systematic literature review (2014, methodology): “A three-layer perceptron (Shrme, 2011) neural network is used in this study by employing back-propagation (Wang, Wang, Zhang, & Guo, 2011) and least-mean-square-error (Sayed, 2003) algorithms to learn the network and adjust the weights, which are initialized randomly .”
Steady-State Mean-Square Error Analysis for Adaptive Filtering under the Maximum Correntropy Criterion (2014, methodology): “The well-known Widrow-Hoff least mean square (LMS) algorithm is based on the minimization of the following MMSE cost [1], [2]: (1) where the error is (2) with being the desired value at time , the weight vector of the adaptive filter, and the input vector, in general defined as (3) Manuscript…”
Variable step-size NLMS and affine projection algorithms (2004, neutral): “I. INTRODUCTION COLORED input data tend to deteriorate the convergenceperformance of least mean square (LMS)-type adaptive filters [1]–[3].”
Estimation of the forgetting factor in kernel recursive least squares (2012, background): “INTRODUCTION The recursive least-squares (RLS) algorithm is one of the most popular adaptive filters of the past few decades [1, 2].”
The Kernel Least-Mean-Square Algorithm (2008, background): “In other words, a small change in the minimum singular value due to small changes of training data may cause a huge difference in the solution.”
Diffusion Least-Mean Squares With Adaptive Combiners: Formulation and Performance Analysis (2010, methodology): “In this section, we analyze the mean-square performance of the CTA diffusion LMS algorithm with adaptive combiners using the energy conservation approach [9], [10], [23], [28], [31].”
Distributed LMS for Consensus-Based In-Network Adaptive Processing (2009, methodology): “Sequential in-time incorporation of sensor observations to enrich the estimation process was considered in [27], in the context of linear least-squares parameter estimation.”
The Quaternion LMS Algorithm for Adaptive Filtering of Hypercomplex Processes (2009, methodology): “Index Terms—Quaternion signal processing, multidimensional adaptive filters, adaptive multi-step ahead prediction, wind modeling, data fusion via vector spaces.”