The complex correntropy is a recently defined similarity measure that extends the advantages of conventional correntropy to complex-valued data. As in the real-valued case, the maximum complex correntropy criterion (MCCC) employs a free parameter called kernel width, which affects the convergence rate, robustness, and steady-state performance of the method. However, determining the optimal value for such parameter is not always a trivial task. Within this context, several works have introduced adaptive kernel width algorithms to deal with this free parameter, but such solutions must be updated to manipulate complex-valued data. This work reviews and updates the most recent adaptive kernel width algorithms so that they become capable of dealing with complex-valued data using the complex correntropy. Besides that, a novel gradient-based solution is introduced to the Gaussian kernel and its respective convergence analysis. Simulations compare the performance of adaptive kernel width algorithms with different fixed kernel sizes in an impulsive noise environment. The results show that the iterative kernel adjustment improves the performance of the gradient solution for complex-valued data.

复数肾上腺素是最近定义的相似性度量，它将传统的肾上腺素的优势扩展到复数值数据。与实值情况一样，最大复数熵准则（MCCC）使用称为内核宽度的自由参数，这会影响该方法的收敛速度，鲁棒性和稳态性能。但是，确定此类参数的最佳值并不总是一件容易的事。在这种情况下，一些工作引入了自适应核宽度算法来处理该自由参数，但是必须更新此类解决方案以处理复数值数据。这项工作回顾并更新了最新的自适应核宽度算法，从而使它们能够使用复数熵来处理复数值数据。除此之外，一种新颖的基于梯度的解决方案被引入到高斯核及其相应的收敛性分析中。仿真比较了在脉冲噪声环境下具有不同固定内核大小的自适应内核宽度算法的性能。结果表明，迭代核调整可提高复杂值数据梯度解决方案的性能。