Variable step-size matrix for the improved multiband-structured subband adaptive filter

2016 ◽  
Author(s):  
Yan Zhenhai ◽  
Yang Feiran ◽  
Yang Jun
Keyword(s):  
Adaptive Filter ◽  
Variable Step Size ◽  
Step Size ◽  
Variable Step ◽  
Subband Adaptive Filter

Variable step-size sign subband adaptive filter with subband filter selection

2018 ◽  
Vol 152 ◽  
pp. 141-147 ◽  
Author(s):  
Jaegeol Cho ◽  
Hyun Jae Baek ◽  
Bum Yong Park ◽  
JaeWook Shin
Keyword(s):  
Adaptive Filter ◽  
Variable Step Size ◽  
Step Size ◽  
Variable Step ◽  
Subband Adaptive Filter ◽  
Filter Selection

Normalised subband adaptive filter with variable step size

2012 ◽  
Vol 48 (4) ◽  
pp. 204 ◽  
Author(s):  
JaeWook Shin ◽  
NamWoong Kong ◽  
PooGyeon Park
Keyword(s):  
Adaptive Filter ◽  
Variable Step Size ◽  
Step Size ◽  
Variable Step ◽  
Subband Adaptive Filter

scholarly journals A Variable Step Size for Acoustic Echo Cancellation Using Normalized Sub band Adaptive Filter

2013 ◽  
Vol 6 (3) ◽  
pp. 449-455
Author(s):  
Harjeet Kaur Ojhla ◽  
Dr. Rajneesh Talwar ◽  
Jyoti Darekar
Keyword(s):  
Steady State ◽  
Convergence Rate ◽  
Adaptive Filter ◽  
Weight Vector ◽  
Variable Step Size ◽  
Mean Square ◽  
Step Size ◽  
Variable Step ◽  
Subband Adaptive Filter ◽  
Normalized Subband Adaptive Filter

Numerous various step size normalized least mean square (VSS-NLMS)Algorithms have been derived to solve the problem of fast convergence rate and low mean square error.Here we find out the ways to control the step size. A normalized subband adaptive filter algorithm uses a fixed and variable step size, which is chosen as a trade-off between the steady-state error and the convergence rate. A variable step size for normalized subband adaptive filter is derived by minimizing the mean-square deviation between the optimal weight vector and the estimated weight vector at each instant of time. The variable step size is presented in terms of error variance. Therefore, we verify thedifferent  algorithmseither they are capable of tracking in stationary and non-stationary environments. The results show good tracking ability and low misalignment of the algorithm in system identification. Parameters are tracking, steady state errors, and misalignment, environment, step size.

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