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- W2016311944 abstract "Among strategies using in distributed adaptive networks diffusion based algorithms despite their scalability, robustness and steady state performance suffer from slow initial convergence. We propose a method to speed up this convergence rate by arranging the network nodes into subgroups, partitioning the tap weight vector and taking advantage of the larger step-size allowed for short filters. As our simulation results show, the proposed algorithm has a faster convergence rate as compared with conventional diffusion LMS algorithm and other algorithms have intended to increase the initial convergence rate of diffusion algorithms." @default.
- W2016311944 created "2016-06-24" @default.
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- W2016311944 date "2013-05-01" @default.
- W2016311944 modified "2023-09-23" @default.
- W2016311944 title "Increasing the initial convergence of distributed diffusion LMS algorithm by a new variable tap-length variable step-size method" @default.
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- W2016311944 doi "https://doi.org/10.1109/iraniancee.2013.6599869" @default.
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