M.S.E. Abadi

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The LMS adaptive filter algorithm can be viewed as the application of a simple iterative linear equation solver to an estimated time variant Wiener-Hopf equation. With this interpretation we can utilize the "bag-of-tricks" available in numerical linear algebra in devising new adaptive filter algorithms. Making use of the preconditioning paradigm, we present(More)
The block LMS algorithms can constitute a major branch in the adaptive algorithms family. In this paper we introduce the new variable step size block least mean square (VSSBLMS) adaptive filter algorithm. The proposed algorithm exhibits fast convergence and lower steady state mean square error when compared to the ordinary BLMS algorithm
Employing a recently introduced framework within which a large number of classical and modern adaptive filter algorithms can be viewed as special cases, we develop a generic variable step size adaptive filter. Variable step-size (VSS) least mean square (VSSLMS), VSS normalized LMS (VSSNLS) and VSS affine projection algorithms (VSSAPA) are particular(More)
In this paper the concepts of selective partial updates (SPU) and selective regressors (SR) in the affine projection (AP) adaptive filtering algorithm are combined and the family of affine projection algorithms with SPU and SR features are established. These algorithms are computationally efficient. We demonstrate the performance of the presented algorithms(More)
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