Distributed Active Noise Control Employing Swish Framework Integrated with Diffusion Adaptive Techniques.

The filtered-x least mean square (F-xLMS) technique dependent distributed active noise control (DANC) system is less effective against outliers and disturbances that represent impulses. In such an environment, DANC algorithms based on Swish functions are being developed to reduce noise. The Swish function overcomes the vanishing gradient problem and accelerates convergence by utilising the smoothness and unboundedness properties. In light of this, the current manuscript presents a set of robust techniques for DANC systems that utilizes the Swish structure in lieu of diffusion strategy for filtered-x Blake Zisserman, filtered-x logarithmic hyperbolic cosine adaptive filter algorithms, filtered-x least mean fourth, and filtered-x generalized maximum correntropy criterion. In regard to the suggested methods, weight updating rules pertaining utilizing to the introduced algorithms are created and a convergence analysis is done. The proposed methods achieve quantitatively 1–5 dB enhancement in noise mitigation in case of varied impulsive noise models and input reference signals considered with a faster convergence compared to previous strategies. [ABSTRACT FROM AUTHOR]