A Particle-Swarm-Optimization-Based Decentralized Nonlinear Active Noise Control System

This paper proposes a functional-link-artificial-neural-network-based (FLANN) multichannel nonlinear active noise control (ANC) system trained using a particle swarm optimization (PSO) algorithm suitable for nonlinear noise processes. The use of PSO algorithm in a multichannel ANC environment not only reduces the local minima problem but also removes the requirement of computationally expensive modeling of the secondary-path transfer functions. A decentralized version of a multichannel nonlinear ANC is also developed, which facilitates scaling up of an existing ANC setup without rederiving the learning rules. This is possible as the controller module of each channel is independent of others. Simulation study of the two new multichannel ANC systems demonstrates comparable mitigation performance. However, the decentralized one is preferred to as it possesses the added advantage of scalability.