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

2012 ◽  
Vol 27 (8) ◽  
pp. 651-666 ◽  
Author(s):  
Jong-Yih Lin ◽  
Chia-Ying Ho
Keyword(s):  
Convergence Analysis ◽  
Exponential Decay ◽  
Noise Control ◽  
Active Noise Control ◽  
Mean Square ◽  
Least Mean Square ◽  
Disturbance Compensation ◽  
Step Size ◽  
Active Noise

Implementation of Single and Multiple Adaptive Step-Size Algorithm to ANC

2007 ◽  
Vol 130 (1) ◽  
Author(s):  
Yesim Sabah ◽  
Masaaki Okuma ◽  
Minoru Okubo
Keyword(s):  
Steady State ◽  
Noise Control ◽  
Active Noise Control ◽  
Mean Square ◽  
Least Mean Square ◽  
Step Size ◽  
Adaptive Step Size ◽  
Active Noise ◽  
Adaptive Step ◽  
State Error

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.

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
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