An Optimized Polynomial Perceptron Network based Adaptive Filter for Non-linear Active Noise Control

Active noise control (ANC) is an efficient way to reduce low frequency noise and is based on the acoustic wave superposition. ANC is popular due to its design flexibility, size and adaptive characteristics. This paper presents a non-linear ANC model called water cycle algorithm (WCA) optimized Chebyshev polynomial perceptron network (PPN). Various other PPNs like Legendre PPN (LPPN) and Chebyshev PPN (CPPN) are also considered for performance assessment. The performance of the proposed WCA optimized CPPN (WCPPN) is evaluated through various performance indexes for a nonlinear primary and secondary path.