Bias-compensated Sparse RLS Algorithms Over Distributed Networks
Bias-compensated Sparse RLS Algorithms Over Distributed Networks
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DOI：
10.23919/ccc55666.2022.9901566
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发表时间：
2022-07
期刊：
影响因子：
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通讯作者：
Senran Peng;Lijuan Jia;S. Kanae;Zi-Jiang Yang
中科院分区：
文献类型：
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作者：
Senran Peng;Lijuan Jia;S. Kanae;Zi-Jiang Yang
在本文中，我们提出了一种基于L1-RLS算法和扩散L1-RLS算法的偏差补偿方法，用于稀疏系统辨识。当输入数据被输入噪声破坏时，我们提出的算法提高了传统 L1-RLS 的估计精度。此外，我们还给出了仿真结果，验证了所提出的算法比其他没有偏差补偿的稀疏RLS算法具有更好的估计精度，也证明了结果在输入噪声下是无偏的。
In this paper, we propose a bias-compensated method based on the L1-RLS algorithm and the diffusion L1-RLS algorithm for sparse system identification. Our proposed algorithms improve the estimation accuracy of traditional L1-RLS when the input data is corrupted by input noises. Furthermore, we give simulation results to verify that proposed algorithms have better estimation accuracy than other sparse RLS algorithms without bias compensation, it also proves that results are unbiased under input noises.