scholarly journals Comparison of Performance and Computational Complexity of Nonlinear Active Noise Control Algorithms

2011 ◽  
Vol 2011 ◽  
pp. 1-9 ◽  
Author(s):  
Mouayad A. Sahib ◽  
Raja Kamil
Keyword(s):  
Computational Complexity ◽  
Noise Control ◽  
Active Noise Control ◽  
Practical Implementation ◽  
Mean Square ◽  
Least Mean Square ◽  
Computational Load ◽  
Active Noise ◽  
Nonlinear Algorithms ◽  
Fxlms Algorithm

Research on nonlinear active noise control (NANC) revolves around the investigation of the sources of nonlinearity as well as the performance and computational load of the nonlinear algorithms. The nonlinear sources could originate from the noise process, primary and secondary propagation paths, and actuators consisting of loudspeaker, microphone or amplifier. Several NANCs including Volterra filtered-x least mean square (VFXLMS), bilinear filtered-x least mean square (BFXLMS), and filtered-s least mean square (FSLMS) have been utilized to overcome these nonlinearities effects. However, the relative performance and computational complexities of these algorithm in comparison to FXLMS algorithm have not been carefully studied. In this paper, systematic comparisons of the FXLMS against the nonlinear algorithms are evaluated in overcoming various nonlinearity sources. The evaluation of the algorithms performance is standardized in terms of the normalized mean square error while the computational complexity is calculated based on the number of multiplications and additions in a single iteration. Computer simulations show that the performance of the FXLMS is more than 80% of the most effective nonlinear algorithm for each type of nonlinearity sources at the fraction of computational load. The simulation results also suggest that it is more advantageous to use FXLMS for practical implementation of NANC.

scholarly journals Active Noise Control Using a Functional Link Artificial Neural Network with the Simultaneous Perturbation Learning Rule

2009 ◽  
Vol 16 (3) ◽  
pp. 325-334 ◽  
Author(s):  
Ya-li Zhou ◽  
Qi-zhi Zhang ◽  
Tao Zhang ◽  
Xiao-dong Li ◽  
Woon-seng Gan
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Noise Control ◽  
Active Noise Control ◽  
Time Varying ◽  
Mean Square ◽  
Least Mean Square ◽  
Functional Link ◽  
Active Noise ◽  
Artificial Neural

In practical active noise control (ANC) systems, the primary path and the secondary path may be nonlinear and time-varying. It has been reported that the linear techniques used to control such ANC systems exhibit degradation in performance. In addition, the actuators of an ANC system very often have nonminimum-phase response. A linear controller under such situations yields poor performance. A novel functional link artificial neural network (FLANN)-based simultaneous perturbation stochastic approximation (SPSA) algorithm, which functions as a nonlinear mode-free (MF) controller, is proposed in this paper. Computer simulations have been carried out to demonstrate that the proposed algorithm outperforms the standard filtered-x least mean square (FXLMS) algorithm, and performs better than the recently proposed filtered-s least mean square (FSLMS) algorithm when the secondary path is time-varying. This observation implies that the SPSA-based MF controller can eliminate the need of the modeling of the secondary path for the ANC system.

scholarly journals Multiple Iteration of Weight Updates for Least Mean Square Adaptive Filter in Active Noise Control Application

2017 ◽  
Vol 95 ◽  
pp. 14006
Author(s):  
Rahimie Mustafa ◽  
Anuar Mikdad Muad ◽  
Shahrizal Jelani ◽  
Ahmad Nur Alifa Abdul Razap
Keyword(s):  
Adaptive Filter ◽  
Noise Control ◽  
Active Noise Control ◽  
Mean Square ◽  
Least Mean Square ◽  
Active Noise ◽  
Control Application

All-pass filtered x least mean square algorithm for narrowband active noise control

2018 ◽  
Vol 142 ◽  
pp. 1-10 ◽  
Author(s):  
Kuheli Mondal (Das) ◽  
Saurav Das ◽  
Aminudin Bin Hj Abu ◽  
Nozomu Hamada ◽  
Hoong Thiam Toh ◽  
...  
Keyword(s):  
Noise Control ◽  
Active Noise Control ◽  
Mean Square ◽  
Least Mean Square ◽  
Active Noise ◽  
Least Mean Square Algorithm

Comparison Study of Active Noise Cancelation Algorithms for Impulsive Noise

2011 ◽  
Author(s):  
Peng Li ◽  
Xun Yu
Keyword(s):  
Impulsive Noise ◽  
Active Noise Control ◽  
Practical Implementation ◽  
Least Mean Square ◽  
Advantages And Disadvantages ◽  
Residual Noise ◽  
Active Noise ◽  
Fxlms Algorithm ◽  
Important Challenge ◽  
M Estimate

Control of impulsive noise is one important challenge for the practical implementation of active noise control (ANC) systems. The advantages and disadvantages of popular filtered-X least mean square (FXLMS) ANC algorithm and nonlinear filtered-X least mean M-estimate (FXLMM) algorithm are discussed in this paper. A new modified FXLMM algorithm is also proposed to achieve better performance in controlling impulsive noise. Computer simulations are carried out for all the three algorithms and the results are presented and analyzed. The results show that the FXLMM and modified FXLMM algorithms are more robust in suppressing the adverse effect of sudden large amplitude impulses than FXLMS algorithm. In particular, the proposed modified FXLMM algorithm can achieve better stability without sacrificing the performance of residual noise when encountering impulses.

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