Adaptive function expansion RLS filters with dynamic selection of channel updates for nonlinear active noise control

In this paper, we propose novel adaptive function expansion recursive least square (RLS) algorithms, which can dynamically choose filter channels for coefficient updates for nonlinear active noise control while still maintaining the compromised performance degradation for nonlinear active noise control. The algorithms are developed based on a multi-channel structure using a channel selection scheme to set the function expansion filter channels active or inactive at each iteration step. The computational complexities are given to confirm the efficiency of the proposed algorithms. Our computer simulations demonstrate that the proposed algorithms will obtain the same performance when compared to their full sequential channel updates.