Charbonnier Quasi Hyperbolic Momentum Spline Based Incremental Strategy for Nonlinear Distributed Active Noise Control

Noise mitigation proves to be a challenging task for active noise control in the existence of nonlinearities. In such environments, functional link neural network (FLN) and adaptive exponential FLN techniques improve the performance of distributed active noise control systems. Nonlinear spline approaches are well known for their low computational complexity and ability to effectively alleviate noise in nonlinear systems. This paper proposes a new cost function for distributed active noise control (DANC) system which is based on the Charbonnier quasi hyperbolic momentum spline (CQHMS) involving incremental approach. This incremental based CQHMS DANC method employs Charbonnier loss and quasi hyperbolic momentum approach which minimizes gradient variance and local crossover points in order to enhance the convergence and steady-state performance. The technique being proposed demonstrates enhanced performance and achieves accelerated convergence when compared to existing techniques in a range of nonlinear DANC scenarios in lieu of varied nonlinear primary path and nonlinear secondary path conditions.