Robust Nonnegative Least Mean Square Algorithm Based on Sigmoid Framework
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摘要: 脉冲噪声会导致非负算法在迭代过程中存在过大的误差值，进而破坏算法的稳定性使其性能严重下降，对此该文提出一种基于Sigmoid框架的非负最小均方算法(SNNLMS)。该算法将传统的非负代价函数嵌入Sigmoid框架中得到新的代价函数，新的代价函数具有抑制脉冲噪声影响的特性。此外，为了增强SNNLMS算法在稀疏系统识别问题上的鲁棒性，该文还提出基于反比例函数的反比例Sigmoid非负最小均方算法(IP-SNNLMS)。仿真结果表明SNNLMS算法有效地解决了脉冲噪声造成的失调问题；IP-SNNLMS增强了算法鲁棒性，改进了算法在稀疏系统识别问题中收敛速率上的缺陷。Abstract: Impulsive noise causes nonnegative algorithms to yield excessive error during iterations, which will damage the stability of the algorithm and causes performance degradation. In the paper, a NonNegative Least Mean Square algorithm based on the Sigmoid framework (SNNLMS) is proposed. The algorithm embeds the conventional nonnegative cost function into the Sigmoid framework to receive a new cost function. The new cost function has the characteristics of suppressing the impact of impulse noise. In addition, in order to enhance the robustness of the SNNLMS algorithm under sparse system identification, the Inversely-Proportional Sigmoid NonNegative Least Mean Square (IP-SNNLMS) is proposed based on the inversely-proportional function. Simulation results demonstrate that the SNNLMS algorithm effectively solves the problem of misadjustment caused by impulsive noise. IP-SNNLMS enhances the robustness of the algorithm and improves the defect of the convergence rate of the SNNLMS algorithm under the sparse system identification.
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