Development and convergence analysis of new least-mean-square-based algorithm equipped with exponential-decay step size and disturbance compensation for active noise control

The purpose of this paper is to investigate a modified adaptive step-size algorithm and implement an active noise control (ANC) system. It is well known that there is a tradeoff between steady state error and convergence rate depending on the step size. This study shows that the new algorithm can track changes in the dynamic characteristics of the ANC system as well as produce a low steady state error. Simulation results are presented to compare the performance of the new algorithm to the basic least mean square (LMS) algorithm. Although there have been several studies of adaptive step-size algorithms, no quantitative analysis has yet been reported for real time active noise control application as far as the authors know. Experimental results are presented for a duct system. The results indicate that the new algorithm provides better performance than the fixed step-size filtered-X least mean square (FXLMS) algorithm.

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M-max partial update leaky bilinear filter-error least mean square algorithm for nonlinear active noise control

Applied Acoustics ◽

10.1016/j.apacoust.2019.07.006 ◽

2019 ◽

Vol 156 ◽

pp. 158-165 ◽

Cited By ~ 2

Author(s):

Dinh Cong Le ◽

Defang Li ◽

Jiashu Zhang

Keyword(s):

Noise Control ◽

Active Noise Control ◽

Mean Square ◽

Least Mean Square ◽

Active Noise ◽

Least Mean Square Algorithm

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

Shock and Vibration ◽

10.1155/2009/587685 ◽

2009 ◽

Vol 16 (3) ◽

pp. 325-334 ◽

Cited By ~ 1

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.

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