Enhanced Adaptive System For Noise Control

Abstract

The source of noise is becoming the important criteria as the environment is affected due to the high reflection of the sound waves. A few algorithms are introduced in this thesis, which can be used to cancel the noise from the environment. The new ANC system is built with a modified FsLMS (MFsLMS) algorithm and along with a PSO-FF hybrid algorithm has been compared with a FLANN-FIR hybrid filter along with FF algorithm and FsLMS algorithm. Nonlinear noise cancellation has been depicted in this dissertation. The developed ANC systems are studied in the presence of different noise signals like Gaussian and Chaotic noise. Simulation results are provided and the performance of each technique under various circumstances are compared with the existing techniques. A modified FsLMS (MFsLMS) algorithm is proposed with a hybrid PSO-FF optimization technique. The hybrid optimization is used to find the stability factor of the system. The proposed method can upgrade the stability of the ANC system. The modification of the existing algorithm is done with Maclaurin series. With this modified method, computing time and complexity can be reduced by comparison with existing methods. Existing methods are used to compare simulation results. The comparison shows that the proposed method has a lower computing complexity compared to the other existing methods. The analysis of the proposed method is performed with two noise signals. Chaotic noise and Gaussian noise signals are chosen. A hybrid combination of functional link artificial neural network and finite impulse response (FLANN-FIR) filter is used with ANC system. In the proposed method, filter coefficients are evaluated through the firefly optimization algorithm. Normalized mean square error (NMSE) value is evaluated through the FsLMS algorithm. The convergence of the system has been enhanced through specific methods. The error value is inversely proportional to the convergence of the system. When the value of the error is reduced, the convergence value has been increased. The simulation results show better convergence of the system. Simulation is carried with same noise signals of Chaotic and Gaussian noises. The proposed method is also compared with the BAT algorithm in which the FF algorithm is replaced by the BAT algorithm. From the simulation results denoised signals are obtained after the implementation of the two methods. The computational complexity has been considerably reduced in comparison with the BAT algorithm.