广义最小误差熵用于自适应滤波
Generalized Minimum Error Entropy for Adaptive Filtering
AI总结：
提出广义误差熵概念，利用广义高斯密度函数作为核函数，推导广义最小误差熵准则及自适应滤波算法，在多种噪声环境下性能良好，并成功应用于声学回声消除。
摘要：
误差熵是一种重要的非线性相似性度量，在许多实际应用中受到越来越多的关注。误差熵准则的默认核函数是高斯核函数，然而，这并不总是最佳选择。在我们的研究中，提出了一种新概念，称为广义误差熵，利用广义高斯密度（GGD）函数作为核函数。我们进一步推导了广义最小误差熵（GMEE）准则，并利用GMEE准则推导出一种新的自适应滤波算法，称为GMEE算法。研究了所提算法的稳定性、稳态性能和计算复杂度。一些仿真表明，GMEE算法在高斯、亚高斯和超高斯噪声环境中均表现良好。最后，将GMEE算法应用于声学回声消除，并取得了良好的效果。
英文摘要：
Error entropy is a important nonlinear similarity measure, and it has received increasing attention in many practical applications. The default kernel function of error entropy criterion is Gaussian kernel function, however, which is not always the best choice. In our study, a novel concept, called generalized error entropy, utilizing the generalized Gaussian density (GGD) function as the kernel function is proposed. We further derivate the generalized minimum error entropy (GMEE) criterion, and a novel adaptive filtering called GMEE algorithm is derived by utilizing GMEE criterion. The stability, steady-state performance, and computational complexity of the proposed algorithm are investigated. Some simulation indicate that the GMEE algorithm performs well in Gaussian, sub-Gaussian, and super-Gaussian noises environment, respectively. Finally, the GMEE algorithm is applied to acoustic echo cancelation and performs well.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
