This paper presents a comparison between the different adaptive algorithms
for designing a cost-effective Anti-Noise Control (ANC) system. Typically, the ANC
systems use linear FIR-adaptive filter Filtered- Least Mean Square (FxLMS)
configuration. FxLMS is straightforward in hardware implementation but, its efficiency
substantially degrades in case time-varying and non-linear acoustic environment and
the probability that it will converge to local minima. Meanwhile, the implementation of
Trigonometric Functional-Linear Adaptive Neural Network (FLANN) enhances ANC
performance for non-linear noise signals. However, at the same time, it increases the
complexity of hardware implementation and is unable to solve the problem of local
minima convergence. Whereas evolutionary algorithms -Genetic Algorithm (GA) and
metaheuristic algorithm Particle Swarm Optimization (PSO) increase the robustness
and stability in non-linear, and the time-varying acoustic environment with absolute
zero probability to converge to local minima. This paper briefly discusses on
implementation of FxLMS, FLANN, PSO, and GA-based ANC systems. Further,
simulation compares Mean Square Error, BER, and PSNR to provide computational
efficiency of these algorithms.
Keywords: Active Noise Control, Adaptive algorithm, Filtered-Least Square
Method (FxLMS), Functional Link Adaptive Neural Network (FLANN), Genetic
Algorithm (GA), MSE, Particle Swarm Optimization (PSO), PSNR, SNR.
