A justification for the improved performance of the multi-split LMS algorithm

This paper presents an analysis that justifies the improved performance of the multi-split LMS algorithm. It is shown that instead of reducing the eigenvalue ratio, the multi-split operation increases the diagonalization factor of the transformed input signal autocorrelation matrix, which assists th...

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Main Authors: Souza, R.D., Resende, L.S., Bellanger, M.G.
Format: Conference Proceeding
Language:English
Subjects:
Algorithm design and analysis
Autocorrelation
Computational complexity
Data preprocessing
Discrete cosine transforms
Eigenvalues and eigenfunctions
Finite impulse response filter
Least squares approximation
Performance evaluation
Robustness
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Resende, L.S.
Bellanger, M.G.
description This paper presents an analysis that justifies the improved performance of the multi-split LMS algorithm. It is shown that instead of reducing the eigenvalue ratio, the multi-split operation increases the diagonalization factor of the transformed input signal autocorrelation matrix, which assists the power normalized and time-varying step-size LMS algorithm used for updating the single parameters independently. Case studies and simulation results enable us to evaluate the improved performance of the multi-split LMS algorithm.
doi_str_mv 10.1109/ICASSP.2003.1201625
format conference_proceeding
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ispartof 2003 IEEE International Conference on Acoustics, Speech, and Signal Processing, 2003. Proceedings. (ICASSP '03), 2003, Vol.6, p.VI-89
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source IEEE Electronic Library (IEL) Conference Proceedings
subjects Algorithm design and analysis
Autocorrelation
Computational complexity
Data preprocessing
Discrete cosine transforms
Eigenvalues and eigenfunctions
Finite impulse response filter
Least squares approximation
Performance evaluation
Robustness
title A justification for the improved performance of the multi-split LMS algorithm
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