Generalized combined nonlinear adaptive filters: From the perspective of diffusion adaptation over networks

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.