scholarly journals Set-membership filtering and a set-membership normalized LMS algorithm with an adaptive step size

1998 ◽  
Vol 5 (5) ◽  
pp. 111-114 ◽  
Author(s):  
S. Gollamudi ◽  
S. Nagaraj ◽  
S. Kapoor ◽  
Yih-Fang Huang
Keyword(s):  
Lms Algorithm ◽  
Step Size ◽  
Adaptive Step Size ◽  
Set Membership ◽  
Adaptive Step ◽  
Normalized Lms

Implementation of Single and Multiple Adaptive Step Size Algorithm to ANC

2004 ◽  
Author(s):  
Yesim Sabah ◽  
Masaaki Okuma ◽  
Minoru Okubo
Keyword(s):  
Steady State ◽  
Noise Control ◽  
Active Noise Control ◽  
Lms Algorithm ◽  
Step Size ◽  
Adaptive Step Size ◽  
Active Noise ◽  
Control Application ◽  
Adaptive Step ◽  
State Error

The purpose of this paper is to investigate a modified adaptive step size algorithm and implement to active noise control (ANC) system. It is well-known that there is a trade-off between steady state error and convergence rate depending on the step size. This study shows that the new algorithm can track changes in dynamic characteristics of ANC system as well as produce a low steady state error. Simulation results are presented to compare the performance of the new algorithm to basic LMS algorithm. Although there have been several studies for adaptive step size algorithm, 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 FXLMS algorithm.

scholarly journals Set-Membership Quaternion Normalized LMS Algorithm

2018 ◽  
Author(s):  
Hamid Reza Moradi ◽  
Akram Zardadi
Keyword(s):  
Wind Profile ◽  
Adaptive Beamforming ◽  
Lms Algorithm ◽  
Mean Square ◽  
Least Mean Square ◽  
Step Size ◽  
Set Membership ◽  
Normalized Least Mean Square ◽  
Update Rate ◽  
Normalized Lms

In this paper, we propose the set-membership quaternion normalized least-mean-square (SM-QNLMS) algorithm. For this purpose, first, we review the quaternion least-mean-square (QLMS) algorithm, then go into the quaternion normalized least-mean-square (QNLMS) algorithm. By having the QNLMS algorithm, we propose the SM-QNLMS algorithm in order to reduce the update rate of the QNLMS algorithm and avoid updating the system parameters when there is not enough innovation in upcoming data. Moreover, the SM-QNLMS algorithm, thanks to the time-varying step-size, has higher convergence rate as compared to the QNLMS algorithm. Finally, the proposed algorithm is utilized in wind profile prediction and quaternionic adaptive beamforming. The simulation results demonstrate that the SM-QNLMS algorithm outperforms the QNLMS algorithm and it has higher convergence speed and lower update rate.

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