scholarly journals Stochastic Behaviour Analysis of Adaptive Averaging Step-size Sign Normalised Hammerstein Spline Adaptive Filtering

2021 ◽  
Vol 6 (1) ◽  
pp. 577-586
Author(s):  
Theerayod Wiangtong ◽  
Sethakarn Prongnuch ◽  
Suchada Sitjongsataporn
Keyword(s):  
Adaptive Filtering ◽  
Step Size ◽  
Stochastic Behaviour ◽  
Behaviour Analysis

A Variable Step Size LMS Adaptive Filtering Algorithm and Simulations Based on Computational Verb Theory

2013 ◽  
Vol 756-759 ◽  
pp. 3972-3976 ◽  
Author(s):  
Li Hui Sun ◽  
Bao Yu Zheng
Keyword(s):  
Adaptive Filtering ◽  
Adaptive Algorithm ◽  
Linear Functional ◽  
Functional Relationship ◽  
Lms Algorithm ◽  
Variable Step Size ◽  
Step Size ◽  
Filtering Algorithm ◽  
Variable Step ◽  
Simulation Results

Based on traditional LMS algorithm, variable step LMS algorithm and the analysis for improved algorithm, a new variable step adaptive algorithm based on computational verb theory is put forward. A kind of sectorial linear functional relationship is established between step parameters and the error. The simulation results show that the algorithm has the advantage of slow change which is closely to zero. And overcome the defects of some variable step size LMS algorithm in adaptive steady state value is too large.

Variable individual step‐size subband adaptive filtering algorithm

2014 ◽  
Vol 50 (3) ◽  
pp. 177-178 ◽  
Author(s):  
Ji‐Hye Seo ◽  
PooGyeon Park
Keyword(s):  
Adaptive Filtering ◽  
Step Size ◽  
Filtering Algorithm ◽  
Individual Step

scholarly journals Development of Multi-Staged Adaptive Filtering Algorithm for Periodic Structure-Based Active Vibration Control System

2019 ◽  
Vol 9 (3) ◽  
pp. 611 ◽  
Author(s):  
Qiu Yang ◽  
Kyeongnak Lee ◽  
Byeongil Kim
Keyword(s):  
Adaptive Filtering ◽  
Periodic Structures ◽  
Lms Algorithm ◽  
Variable Step Size ◽  
Complex Frequency ◽  
Step Size ◽  
Initial State ◽  
Frequency Spectra ◽  
Filtering Algorithm ◽  
Active Vibration

A digital adaptive filtering system is applied to various fields such as current disturbance, noise cancellation, and active vibration and noise control. The least mean squares (LMS) algorithm is widely adopted, owing to its simplicity and low computational burden. A limitation of the LMS algorithm with fixed step size is the trade-off between convergence speed and stability. Several studies have tried to overcome this limitation by varying the step size according to filter input and error; however, the related algorithms with variable step size have not been suitable for signals with complex frequency spectra. As the error decreases, the quality of the output signal deteriorates due to the increase in the higher-order components, depending on the characteristics of the algorithm. Therefore, a novel adaptive filtering algorithm was proposed to overcome these drawbacks. It increased the stability of the system by decreasing the step size using an exponential function. In addition, the error was reduced through normalization using the power of the input signal in the initial state, and the misadjustments in the system were adjusted properly by introducing an energy autocorrelation function of instantaneous error. Furthermore, a novel multi-staged adaptive LMS (MSA-LMS) algorithm was introduced and applied to active periodic structures. The proposed algorithm was validated by simulation and observed to be superior to the conventional LMS algorithms. The results of this study can be applied to active control systems for the reduction of vibration and noise signals with complex spectra in next-generation powertrains, such as hybrid and electric vehicles.

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