扩散- klms算法

2014 17th International Conference on Computer and Information Technology (ICCIT) Pub Date : 2014-12-22 DOI:10.1109/ICIT.2014.33

R. Mitra, V. Bhatia

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引用次数: 17

摘要

在分布式网络中，扩散最小均方(LMS)[1]算法比原始LMS算法收敛速度更快。此外，它优于其他分布式LMS算法，如空间LMS和增量LMS[2]。然而，LMS和扩散-LMS都不适用于数据可能不可线性分离的非线性环境[3]。针对这种非线性，[3]中提出了LMS的一种变体，称为核LMS (KLMS)。我们打算在本文中提出扩散- lms的核化版本。

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The Diffusion-KLMS Algorithm

The diffusion least mean squares (LMS) [1] algorithm gives faster convergence than the original LMS in a distributed network. Also, it outperforms other distributed LMS algorithms like spatial LMS and incremental LMS [2]. However, both LMS and diffusion-LMS are not applicable in non-linear environments where data may not be linearly separable [3]. A variant of LMS called kernel-LMS (KLMS) has been proposed in [3] for such non-linearities. We intend to propose the kernelised version of diffusion-LMS in this paper.

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2014 17th International Conference on Computer and Information Technology (ICCIT)

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