Yi Yu

TL;DR: In this article, robust diffusion recursive least-squares algorithms are proposed to mitigate the performance degradation often experienced in networks of agents in the presence of impulsive noise, where a time-dependent constraint on the squared norm of the intermediate update at each node is computed using side information from the neighboring nodes to further improve the robustness.

TL;DR: The proposed algorithms not only inherit the good robustness of sign algorithm against impulsive interferences, but also obtain a significant improvement in the performance as compared to their counterparts (i.e., SSAF and IPSSAF), in terms of the convergence rate and tracking capability.

TL;DR: The development of the linear active noise control (ANC) technique in the past decade was reviewed, with recent advances of NLANC algorithms, such as spline ANC algorithms, kernel adaptive filters, and nonlinear distributed ANC algorithms.

TL;DR: Simulation results indicate that the proposed IAF-MPAPA outperforms the PAPA, IPA, and memory IPAPA (MIPAPA) in terms of the convergence rate and tracking capability when the unknown impulse response suddenly changes.

TL;DR: Simulation results have demonstrated that the proposed algorithm not only has better tracking capability than the existing subband adaptive filter algorithms, but also exhibits lower steady-state error.