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- W2073171707 abstract "Considerable savings in hardware and computation time can be achieved very simply by slowing down the adaptive algorithm for an adaptive digital filter either by updating weights loss often than every sample (typically every n samples, where n is the number of fitter weights), or by updating only one weight per sample cyclically repeating every n samples. This technique has the advantage of being useful with any of the many adaptive algorithms including the LMS non-recursive algorithm and the Stearns-White recursive algorithm. Experiments verify that the slowed down algorithms not only save hardware and computation time, but actually improve filter performance." @default.
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- W2073171707 date "1982-07-01" @default.
- W2073171707 modified "2023-09-27" @default.
- W2073171707 title "Experimental performance data for slowed-down adaptive algorithms†" @default.
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- W2073171707 doi "https://doi.org/10.1080/00207218208901487" @default.
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