Abstract This paper derives a collaborative control algorithm called generalized collaborative functional link adaptive filter (GCFLAF) for nonlinear active noise control (NANC). The algorithm adopts the collaborative scheme by handling the noise cancellation problem divisionally and parallelly, and working coordinately. It takes the characteristics of different nonlinear components into consideration and handles them separately. And a hierarchical adaption law is adopted in which linear filter is always prior to nonlinear filters no matter whether nonlinear distortions arise from the primary path as required by the filtering scenario. Thus the proposed scheme can tailor to different nonlinear characters arising from the primary path and can hierarchically adapt to the influences of the distinct nonlinearities. Particularly, since the zero-memory nonlinearity and memory nonlinearity are often unavoidable in NANC, it employs two shrinkage parameters to regulate the contribution of the different degrees of memory and zero-memory nonlinearity as required by the filtering scenario, aiming to achieve a better convergence performance and robustness to nonlinear distortions. Some numerical simulation results in the context of random noises as well as the real noise signals verify the improved convergence behavior of the presented architecture in NANC scenarios.

中文翻译：

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一种用于非线性有源噪声控制的广义协同函数链路自适应滤波器

摘要 本文推导出一种用于非线性有源噪声控制（NANC）的协同控制算法，称为广义协同功能链路自适应滤波器（GCFLAF）。该算法采用协同方案，分工并行处理噪声消除问题，协同工作。它考虑了不同非线性元件的特性，分别处理。并且采用分层自适应律，其中线性滤波器总是在非线性滤波器之前，无论非线性失真是否按照滤波场景的要求从主路径产生。因此，所提出的方案可以适应由主要路径引起的不同非线性特征，并且可以分层适应不同非线性的影响。特别，由于零记忆非线性和记忆非线性在NANC中往往是不可避免的，它采用两个收缩参数来根据滤波场景的要求调节不同程度的记忆和零记忆非线性的贡献，旨在获得更好的收敛性能和对非线性失真的鲁棒性。在随机噪声和真实噪声信号环境中的一些数值模拟结果验证了所提出的架构在 NANC 场景中改进的收敛行为。旨在实现更好的收敛性能和对非线性失真的鲁棒性。在随机噪声和真实噪声信号环境中的一些数值模拟结果验证了所提出的架构在 NANC 场景中改进的收敛行为。旨在实现更好的收敛性能和对非线性失真的鲁棒性。在随机噪声和真实噪声信号环境中的一些数值模拟结果验证了所提出的架构在 NANC 场景中改进的收敛行为。