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A Theory on the Convergence Behavior of the Affine Projection Algorithm
IEEE Transactions on Signal Processing, 2011In this paper, we present a theoretical convergence analysis of the affine projection algorithm (APA) based on the arguments of energy conservation. Although the APA and its convergence analysis have been widely studied, the dependency of weight-error vector on past noise is usually neglected for simplicity.
Kim, SE, Lee, JW, Song, WJ
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The data-selective constrained affine-projection algorithm
2001 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings (Cat. No.01CH37221), 2001This paper introduces a constrained version of the recently proposed set-membership affine projection algorithm based on the set-membership criteria for coefficient update. The algorithm is suitable for linearly constrained minimum-variance filtering applications.
Stefan Werner 0001 +2 more
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Affine projection algorithms for sparse system identification
2013 IEEE International Conference on Acoustics, Speech and Signal Processing, 2013We propose two versions of affine projection (AP) algorithms tailored for sparse system identification (SSI). Contrary to most adaptive filtering algorithms devised for SSI, which are based on the l1 norm, the proposed algorithms rely on homotopic l0 norm minimization, which has proven to yield better results in some practical contexts.
Markus V. S. Lima +2 more
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2015
The normalized least-mean-squares (NLMS) algorithm has a problem that the convergence slows down for correlated input signals. The reason for this phenomenon is explained by looking at the algorithm from a geometrical point of view. This observation motivates the affine projection algorithm (APA) as a natural generalization of the NLMS algorithm.
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The normalized least-mean-squares (NLMS) algorithm has a problem that the convergence slows down for correlated input signals. The reason for this phenomenon is explained by looking at the algorithm from a geometrical point of view. This observation motivates the affine projection algorithm (APA) as a natural generalization of the NLMS algorithm.
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An efficient implementation of the kernel affine projection algorithm
2013 8th International Symposium on Image and Signal Processing and Analysis (ISPA), 2013In this paper an efficient kernel affine projection algorithm using dichotomous coordinate descent iterations is proposed. The effectiveness of the proposed algorithm for nonlinear system identification and forward prediction is confirmed by computer simulations.
Felix Albu +3 more
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A block exact fast affine projection algorithm
IEEE Transactions on Speech and Audio Processing, 1999This paper describes a block (affine) projection algorithm that has exactly the same convergence rate as the original sample-by-sample algorithm and smaller computational complexity than the fast affine projection algorithm. This is achieved by (1) introducing a correction term that compensates for the filter output difference between the sample-by ...
Masashi Tanaka +2 more
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A class of quaternion valued affine projection algorithms
Signal Processing, 2013The strictly linear quaternion valued affine projection algorithm (QAPA) and its widely linear counterpart (WLQAPA) are introduced, in order to provide fast converging stochastic gradient learning in the quaternion domain, for the processing of both second order circular (proper) and second order noncircular (improper) signals.
Jahanchahi, C +2 more
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An Affine Projection Algorithm With Update-Interval Selection
IEEE Transactions on Signal Processing, 2013This paper presents a mean-square deviation (MSD) analysis of the periodic affine projection algorithm (P-APA) and two update-interval selection methods to achieve improved performance in terms of the convergence and the steady-state error. The MSD analysis of the P-APA considers the correlation between the weight error vector and the measurement noise
JaeWook Shin +3 more
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A Class of Affine Projection Algorithms
2011Affine projection algorithms (APAs) are very good candidates for echo cancellation. The two main reasons for that are: they may converge and track much faster than the NLMS algorithm and they can be efficient from an arithmetic complexity viewpoint. In this chapter, we derive some useful APAs for SAEC with the WL model.
Jacob Benesty +3 more
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Adaptive combination of affine projection and NLMS algorithms
Signal Processing, 2014We propose a novel scheme for combining two adaptation terms of affine projection algorithms with different projection orders and step-sizes. The selection of the mixing parameter that determines the performance of the proposed combination scheme is derived by the largest decrease of the mean-square deviation.
Choi, JH, Kim, SH, Kim, SW
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