Abstract Adaptive filtering algorithms have been widely used in many areas, among which the minimum error entropy (MEE) algorithm is a superior choice, due to its excellent performance in the non-Gaussian noise situations. However, the computational complexity of the MEE algorithm is expensive, which leads to the computational bottlenecks, especially for large-scale datasets. In order to address the problem, we propose an adaptive filtering algorithm based on the quantized minimum error entropy (QMEE) criterion with an online quantization method, named QMEE algorithm. Moreover, we analyze the transient behavior characteristic and derive an approximate analytical expression for the steady-state excess mean square error (EMSE) based on the Taylor expansion. The extensive simulation results in linear modeling and electroencephalogram (EEG) denoising task demonstrate that the proposed method can outperform other robust adaptive filtering algorithms.

中文翻译：

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具有量化最小误差熵准则的自适应滤波

摘要 自适应滤波算法在许多领域得到了广泛的应用，其中最小误差熵（MEE）算法在非高斯噪声情况下具有优异的性能，是一种较好的选择。然而，MEE算法的计算复杂度昂贵，导致计算瓶颈，特别是对于大规模数据集。为了解决这个问题，我们提出了一种基于量化最小误差熵（QMEE）准则和在线量化方法的自适应滤波算法，称为QMEE算法。此外，我们分析了瞬态行为特征，并基于泰勒展开导出了稳态超均方误差 (EMSE) 的近似解析表达式。