Generalized minimum error entropy for robust learning
Generalized minimum error entropy for robust learning
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DOI：
10.1016/j.patcog.2022.109188
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发表时间：
2022-11
期刊：
影响因子：
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通讯作者：
Jiacheng He;G. Wang;Kui Cao;He Diao;Guotai Wang;Bei Peng
中科院分区：
文献类型：
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作者：
Jiacheng He;G. Wang;Kui Cao;He Diao;Guotai Wang;Bei Peng
误差熵（EE）的应用有时会受到限制，因为它的形状不能通过默认的高斯核函数灵活地调整以适应噪声的变化，从而降低了基于最小误差熵（EE）准则的算法的性能。本文引入广义高斯密度（GGD）作为核函数，提出了广义EE（GEE），以提高EE的鲁棒性，并通过量化GEE（QGEE）进一步改进GEE，以减少其计算量。在广义最小误差熵和量化广义最小误差熵的基础上，提出了广义最小误差熵和量化广义最小误差熵两种学习准则，并在此基础上提出了新的自适应滤波、核递归最小二乘和多层感知器.数值仿真结果表明，所提出的算法的性能优于传统的基于小波变换的算法。
The applications of error entropy (EE) are sometimes limited because its shape cannot be flexibly adjusted by the default Gaussian kernel function to adapt to noise variation and thus lowers the performance of algorithms based on minimum error entropy (MEE) criterion. In this paper, a generalized EE (GEE) is proposed by introducing the generalized Gaussian density (GGD) as its kernel function to improve the robustness of EE. In addition, GEE can be further improved to reduce its computational load by the quantized GEE (QGEE). Furthermore, two learning criteria, called generalized minimum error entropy (GMEE) and quantized generalized minimum error entropy (QGMEE), are developed based on GEE and QGEE, and new adaptive filtering (AF), kernel recursive least squares (KRLS), and multilayer perceptron (MLP) based on the proposed criteria are presented. Several numerical simulations indicate that the performance of proposed algorithms performs better than that of algorithms based on MEE.