AI 中文总结
研究通信延迟下分布式多通道有源噪声控制收敛慢问题，提出引入自适应动量项的 AMAS-MGDFxLMS 算法，利用余弦相似度动态调整动量参数，实现更快收敛且保持稳定有效的降噪性能。
AI 中文摘要
分布式多通道有源噪声控制（DMCANC）通过在多个节点间分配处理任务减轻了集中式 ANC 系统的计算负担，但需信息交换以实现满意的全局降噪。为提高通信延迟下的鲁棒性，提出了自动收缩步长混合梯度滤波参考 LMS（ASSS-MGDFxLMS）算法。然而，减小的步长不可避免地减缓了收敛速度。本文引入自适应动量项来加速收敛，利用余弦相似度评估瞬时梯度与动量分量的对齐情况并动态调整动量参数。该设计在方向一致时加速收敛，同时在延迟通信下保持稳定性。仿真结果表明，所提出的自适应动量 ASSS-MGDFxLMS（AMAS-MGDFxLMS）算法比 ASSS-MGDFxLMS 收敛更快，同时保持稳定有效的降噪性能。
英文摘要
Distributed multichannel active noise control (DMCANC) reduces the computational burden of centralized ANC systems by distributing processing tasks across multiple nodes, while requiring information exchange to achieve satisfactory global noise reduction. To improve robustness under communication delays, the auto-shrink step size mixed-gradients filtered reference LMS (ASSS-MGDFxLMS) algorithm has been proposed. However, the reduced step size inevitably slows convergence. In this work, an adaptive momentum term is introduced to accelerate convergence, where cosine similarity is used to evaluate the alignment between the instantaneous gradient and the momentum component and dynamically adjust the momentum parameter. This design accelerates convergence when the directions are consistent while preserving stability under delayed communication. Simulation results demonstrate that the proposed adaptive momentum ASSS-MGDFxLMS (AMAS-MGDFxLMS) algorithm achieves faster convergence than ASSS-MGDFxLMS while maintaining stable and effective noise reduction performance.
