为了提高非高斯噪声环境下混沌时间序列的预测精度，提出了一种基于自适应 矩估计的最大相关熵算法(AdamMCC)． 在 AdamMCC 中，采用最大相关熵准则作为代价 函数有效地抑制了异常噪声值对预测性能的影响，利用代价函数梯度的一阶矩和二阶矩 估计自适应调整算法的权重参数，在不同阶段为算法提供了更好的最优权重搜索方向，从 而提高了 AdamMCC 的预测性能． 采用 Mackey-Glass 和 Lorenz 两类混沌时间序列进行仿 真实验，验证文中提出的 AdamMCC 的收敛性能和稳态性能． 实验结果表明，在非高斯环 境下的预测过程中，相比于最小均方算法、最大相关熵算法和分数阶最大相关熵算法，文 中提出的基于自适应矩估计的最大相关熵算法在保持鲁棒性的同时，还能以合理的计算 复杂度获得更高的预测精度．

A novel adaptive moment estimation algorithm based on maximum correntropy criterion (AdamMCC) was proposed to improve the prediction accuracy of chaotic sequence in the non-Gaussian noises． The maximum correntropy criterion was chosen as the cost function of the proposed AdamMCC owing to its robustness against non- Gaussian noises． The first and second moments of gradients in the cost function were used to adjust the weight of the parameters in the algorithm，which provides a better search direction for the optimal weight，thus improved the prediction performance of the proposed AdamMCC． Simulations on the prediction of the Mackey-Glass chaotic time sequence and Lorenz chaotic time sequence illustrate that the proposed AdamMCC can achieve better prediction performance with affordable computational complexity and maintain robustness，compared with the least mean square algorithm (LMS)，the maximum correntropy criterion algorithm (MCC)，and the fractional-order maxi- mum correntropy criterion algorithm (FMCC) in the presence of non-Gaussian noises．