论文标题
扩散归一化最小平均M估计算法的研究
Study of Diffusion Normalized Least Mean M-estimate Algorithms
论文作者
论文摘要
这项工作提出扩散基于修改后的Huber函数将最低平均M-算法归一化算法，该算法可以在脉冲干扰的情况下为分布式网络配备具有强大的学习能力。为了利用系统的基本稀疏以进一步提高学习性能，还通过将估计值的$ l_0 $ norm纳入更新过程中，从而开发出稀疏感知的变体。然后，我们分析统一框架中算法的瞬态，稳态和稳定性行为。特别是，我们提出了一种分析方法，它比处理分数功能的常规方法更简单，因为它消除了积分和价格定理的要求。在各种冲动的噪声场景中的模拟表明，所提出的算法优于某些现有的扩散算法，并且可以验证理论结果。
This work proposes diffusion normalized least mean M-estimate algorithm based on the modified Huber function, which can equip distributed networks with robust learning capability in the presence of impulsive interference. In order to exploit the system's underlying sparsity to further improve the learning performance, a sparse-aware variant is also developed by incorporating the $l_0$-norm of the estimates into the update process. We then analyze the transient, steady-state and stability behaviors of the algorithms in a unified framework. In particular, we present an analytical method that is simpler than conventional approaches to deal with the score function since it removes the requirements of integrals and Price's theorem. Simulations in various impulsive noise scenarios show that the proposed algorithms are superior to some existing diffusion algorithms and the theoretical results are verifiable.
