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LMS algorithm with gradient descent filter length
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DOI：10.1109/LSP.2003.822892.png)
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Abstract
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This letter presents a novel variable-length least mean square algorithm, whose filter length is adjusted dynamically along the negative gradient direction of the squared estimation error. Compared with other variable-length algorithms, the proposed algorithm has faster convergence and more robust performance in diverse environments.
Keywords:
filter length
gradient descent
least mean square (LMS)
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