融合快速响应与低稳态误差优势，提升主动降噪系统稳定性。
A Stabilized Hybrid Active Noise Control Algorithm of GFANC and FxNLMS with Online Clustering
- 用GFANC初始化FxNLMS，再由FxNLMS持续自适应调整
- 稳态误差极低，响应速度快，系统运行稳定
- 适合需要快速降噪且长期稳定的工业场景
滤波-x归一化最小均方（FxNLMS）算法虽能实现低稳态误差，但收敛慢且易发散；而生成式固定滤波主动降噪（GFANC）响应快，但缺乏自适应能力导致稳态误差大。本文提出一种混合GFANC-FxNLMS算法，利用GFANC在帧级提供初始控制滤波器，由FxNLMS在采样率上持续自适应。为避免GFANC滤波器微小变化反复重置FxNLMS导致系统不稳定，引入在线聚类模块，减少不必要的重初始化。仿真结果表明，该算法兼具快速响应、极低稳态误差和高稳定性，仅需一个预训练的宽带滤波器即可实现。
原文摘要 · Abstract (English)
The Filtered-x Normalized Least Mean Square (FxNLMS) algorithm suffers from slow convergence and a risk of divergence, although it can achieve low steady-state errors after sufficient adaptation. In contrast, the Generative Fixed-Filter Active Noise Control (GFANC) method offers fast response speed, but its lack of adaptability may lead to large steady-state errors. This paper proposes a hybrid GFANC-FxNLMS algorithm to leverage the complementary advantages of both approaches. In the hybrid GFANC-FxNLMS algorithm, GFANC provides a frame-level control filter as an initialization for FxNLMS, while FxNLMS performs continuous adaptation at the sampling rate. Small variations in the GFANC-generated filter may repeatedly reinitialize FxNLMS, interrupting its adaptation process and destabilizing the system. An online clustering module is introduced to avoid unnecessary re-initializations and improve system stability. Simulation results show that the proposed algorithm achieves fast response, very low steady-state error, and high stability, requiring only one pre-trained broadband filter.
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