scholarly journals Nonlinear Neural System for Active Noise Controller to Reduce Narrowband Noise

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Minh-Canh Huynh ◽  
Cheng-Yuan Chang
Keyword(s):  
Active Noise Control ◽  
Neural System ◽  
Stability And Convergence ◽  
Least Mean Square ◽  
Neural Controller ◽  
Active Noise ◽  
Fxlms Algorithm ◽  
Simulation Results ◽  
The Stability ◽  
Narrowband Noise

Noise in a dynamic system is practically unavoidable. Today, such noise is commonly reduced using an active noise control (ANC) system with the filtered-x least mean square (FXLMS) algorithm. However, the performance of the ANC system with FXLMS algorithm is significantly impaired in nonlinear systems. Therefore, this paper develops an efficient nonlinear adaptive feedback neural controller (NAFNC) to eliminate narrowband noise for both linear and nonlinear ANC systems. The proposed controller is implemented to update its coefficients without prior offline training by neural network. Hence, the proposed method has rapid convergence rate as confirmed by simulation results. The proposed work also analyzes the stability and convergence of the proposed algorithm. Simulation results verify the effectiveness of the proposed method.

scholarly journals ACTIVE NOISE CONTROL USING NEURAL SYSTEM

2010 ◽  
Vol 13 (3) ◽  
pp. 67-74
Author(s):  
Tuan Van Huynh ◽  
Nghĩa Hoai Duong
Keyword(s):  
Noise Control ◽  
Acoustic Noise ◽  
Active Noise Control ◽  
Neural System ◽  
Control Area ◽  
New Method ◽  
Traditional Methods ◽  
Active Noise ◽  
Opposite Phase ◽  
Simulation Results

The principle of active noise control (ANC) is to produce a secondary acoustic noise which has the same magnitude as the unwanted primary noise but with opposite phase. The sum of these two signals reduces acoustic noise in the noise control area. In this paper we present a new ANC method using neural system. Moreover a new method for compensating the saturation of the power applifier is also introduced. The performance of the proposed method is compared to that of traditional methods. Simulation results are provided for illustration.

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.

Active Noise Cancellation for a Three-Dimensional Enclosure by Using Multiple-Channel Adaptive Control and H∞ Control

1998 ◽  
Vol 120 (4) ◽  
pp. 958-964 ◽  
Author(s):  
M. R. Bai ◽  
Z. Lin
Keyword(s):  
Digital Signal Processor ◽  
Random Noise ◽  
Active Noise Control ◽  
Three Dimensional ◽  
Internal Field ◽  
Least Mean Square ◽  
Control Structures ◽  
Multiple Channel ◽  
Active Noise ◽  
Fxlms Algorithm

Active noise control (ANC) techniques for a three-dimensional enclosure are compared in terms of two control structures and two control algorithms. The multiple-channel filtered-x least-mean-square (FXLMS) algorithm and the H∞ robust control algorithm are employed for controller synthesis. Both feedforward and feedback control structures are compared. The Youla’s parameterization is employed in the formulation of the multiple-channel feedback FXLMS algorithm. The algorithms are implemented using a floating-point digital signal processor (DSP). Experiments are carried out to validate the ANC approaches for attenuation of the internal field in a rectangular wooden box. Position and number of actuators and sensors are also investigated. A broadband random noise and an engine noise are chosen as the primary noises in the experiments. The experimental results indicate that the feedforward structure yields a broader band of attenuation than the feedback structure. The FXLMS control and H∞, control achieve comparable performance.

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.

Hybrid Active Noise Control of a One-Dimensional Acoustic Duct

2001 ◽  
Vol 124 (1) ◽  
pp. 10-18 ◽  
Author(s):  
E. Esmailzadeh ◽  
A. Alasty ◽  
A. R. Ohadi
Keyword(s):  
Noise Control ◽  
Active Noise Control ◽  
Closed Form Solution ◽  
Form Solution ◽  
Least Mean Square ◽  
One Dimensional ◽  
Acoustic Feedback ◽  
Active Noise ◽  
Adaptive Weight ◽  
Fxlms Algorithm

Based on the closed-form solution of a one-dimensional wave equation, the primary, secondary and acoustic feedback paths for the active control of sound in an acoustic duct have been investigated. Accurate models for the condenser microphone and loudspeaker, which include both the electro-mechanical and mechano-acoustical couplings as well as acoustical damping, have been considered. A generalized form of the filtered-x least mean square (FXLMS) algorithm that uses a more general recursive adaptive weight update equation to improve the performance of the FXLMS algorithm has been developed. Computer simulations were carried out to investigate the performance of acoustical feedback and feedback neutralization as well as the effect of boundary conditions on the performance of active noise control (ANC) systems. Comparisons of the simulation results were carried out.

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 Stability and Convergence Analysis of Direct Adaptive Inverse Control

Complexity ◽  
2017 ◽  
Vol 2017 ◽  
pp. 1-12 ◽  
Author(s):  
Muhammad Shafiq ◽  
Muhammad A. Shafiq ◽  
Hassan A. Yousef
Keyword(s):  
Computer Simulation ◽  
Convergence Analysis ◽  
Indirect Method ◽  
Stability And Convergence ◽  
Adaptive Inverse Control ◽  
Inverse Control ◽  
Simulation Results ◽  
The Stability ◽  
Feed Forward Controller ◽  
Physical Plants

In adaptive inverse control (AIC), adaptive inverse of the plant is used as a feed-forward controller. Majority of AIC schemes estimate controller parameters using the indirect method. Direct adaptive inverse control (DAIC) alleviates the adhocism in adaptive loop. In this paper, we discuss the stability and convergence of DAIC algorithm. The computer simulation results are presented to demonstrate the performance of the DAIC. Laboratory scale experimental results are included in the paper to study the efficiency of DAIC for physical plants.

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