Abstract Combination of nonlinear adaptive filters (CNAF) is gaining popularity as an effective solution to enhance the filter performance by addressing compromises with respect to parameter and structure settings. However, most existing algorithms study the optimal choice of different filters while neglecting the internal structure optimization of filters. This limitation restricts the performance of CNAF. In this work, we propose a new scheme of the CNAF from the perspective of diffusion adaptive over network. Instead of combining the filters in a parallel manner, we consider to organize the candidate filters in a network manner. Specifically, a network with two subnetworks is constructed, and nodes in each subnetwork serve as linear adaptive filters and nonlinear adaptive filters respectively. A generalized CNAF (GCNAF) is obtained by linking the nodes of the network. The proposed GCNAF allows information exchange and sharing among nodes, so as to enhance the performance of the combined filters. As a result, search direction of each filter is also adjusted by combining those of other filters via the diffusion over networks. We show some representative, state-of-the-art combined adaptive filters are special cases of the proposed framework. Simulations with an acoustic problem demonstrate the effectiveness of the proposed GCNAF.

中文翻译：

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广义组合非线性自适应滤波器：从网络上的扩散适应的角度

摘要 非线性自适应滤波器 (CNAF) 的组合作为通过解决参数和结构设置方面的折衷来提高滤波器性能的有效解决方案越来越受欢迎。然而，现有的大多数算法只研究不同滤波器的最优选择，而忽略了滤波器内部结构的优化。此限制限制了 CNAF 的性能。在这项工作中，我们从网络扩散自适应的角度提出了一种新的 CNAF 方案。我们考虑以网络方式组织候选过滤器，而不是以并行方式组合过滤器。具体来说，构建了一个包含两个子网络的网络，每个子网络中的节点分别作为线性自适应滤波器和非线性自适应滤波器。通过链接网络节点获得广义 CNAF (GCNAF)。提出的 GCNAF 允许节点之间的信息交换和共享，以提高组合过滤器的性能。因此，每个过滤器的搜索方向也通过网络上的扩散组合其他过滤器的搜索方向来调整。我们展示了一些具有代表性的、最先进的组合自适应滤波器是所提出框架的特例。具有声学问题的模拟证明了所提出的 GCNAF 的有效性。最先进的组合自适应滤波器是所提出框架的特例。具有声学问题的模拟证明了所提出的 GCNAF 的有效性。最先进的组合自适应滤波器是所提出框架的特例。具有声学问题的模拟证明了所提出的 GCNAF 的有效性。