Distributed Incremental Leaky LMS
Distributed Incremental Leaky LMS
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DOI：
10.1109/iccsp.2015.7322822
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发表时间：
2015-04
期刊：
影响因子：
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通讯作者：
M. Sowjanya;A. Sahoo;Sananda Kumar
中科院分区：
文献类型：
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作者：
M. Sowjanya;A. Sahoo;Sananda Kumar
自适应算法应用于分布式网络，赋予网络自适应能力。增量策略是最简单的协作模式，因为它需要较少的节点之间的通信量。自适应增量策略是基于LMS算法的分布式网络中的一种自适应增量策略，在非理想或实际应用中，其参数估计会出现漂移问题。由于量化误差的不断积累、有限精度误差以及输入序列的频谱激励不足或条件不良，会出现参数估计的漂移或发散问题。它们会导致溢出和接近奇异的自相关矩阵，这会引起参数估计的缓慢逃逸。该方法使用Leaky LMS算法，该算法在更新方程中引入泄漏因子，从而防止权重因能量泄漏而无界。
Adaptive algorithms are applied to distributed networks to endow the network with adaptation capabilities. Incremental Strategy is the simplest mode of cooperation as it needs less amount of communication between the nodes. The adaptive incremental strategy which is developed for distributed networks using LMS algorithm, suffers from drift problem, where the parameter estimate will go unbounded in non ideal or practical implementations. Drift problem or divergence of the parameter estimate occurs due to continuous accumulation of quantization errors, finite precision errors and insufficient spectral excitation or ill conditioning of input sequence. They result in overflow and near singular auto correlation matrix, which provokes slow escape of parameter estimate to go unbound. The proposed method uses the Leaky LMS algorithm, which introduces a leakage factor in the update equation, and so prevents the weights to go unbounded by leaking energy out.