The maximum correntropy criterion (MCC) algorithm has popularly been used in suppressing impulsive noise. It mainly relies on the choice of step size and kernel width. With the study of step size, this paper proposes a novel algorithm that updates the step size based on constructing a variable step size function that uses the moving weighted average algorithm to keep performance stable and sigmoid function to speed the convergence rate. The proposed algorithm is robust against impulsive noises and generates a large step size to accelerate the convergence in iteration beginning, and system mutation makes the system identification performance more stable when the error is small. Based on the improved variable kernel width maximum entropy criterion (IVKW‐MCC) algorithm, which overcomes the shortcoming of how to choose a reliable kernel width in the MCC algorithm, simulation results are compared with other MCC algorithms under impulsive noise interference. Simulations in the system identification scenarios show that the proposed algorithm has better performance in improving the convergence speed over other algorithms. © 2020 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.

中文翻译：

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具有改进的可变内核宽度的最大熵准则算法的可变步长

最大熵准则（MCC）算法已广泛用于抑制脉冲噪声。它主要取决于步长和内核宽度的选择。通过对步长的研究，提出了一种新颖的算法，该算法基于构造可变步长函数来更新步长，该函数使用移动加权平均算法来保持性能稳定，并采用S形函数来加快收敛速度​​。所提出的算法对脉冲噪声具有鲁棒性，并产生较大的步长以加快迭代开始时的收敛速度，并且系统的变异使系统的识别性能在误差较小时更加稳定。基于改进的可变核宽度最大熵准则（IVKW-MCC）算法，克服了MCC算法中选择可靠的核宽度的缺点，在脉冲噪声干扰下，将仿真结果与其他MCC算法进行了比较。在系统识别场景中的仿真表明，该算法在提高收敛速度方面优于其他算法。©2020日本电气工程师学会。由Wiley Periodicals LLC发布。