scholarly journals The New Normalized Subband Adaptive Filter Algorithms with Variable Step-Size

Automatika ◽  
2014 ◽  
Vol 55 (2) ◽  
pp. 188-198
Author(s):  
Mohammad Shams Esfand Abadi ◽  
Mohammad Saeed Shafiee
Keyword(s):  
Adaptive Filter ◽  
Variable Step Size ◽  
Step Size ◽  
Variable Step ◽  
Subband Adaptive Filter ◽  
Normalized Subband Adaptive Filter ◽  
Filter Algorithms

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.

scholarly journals A Sequential Selection Normalized Subband Adaptive Filter with Variable Step-Size Algorithms

2018 ◽  
Vol 2018 ◽  
pp. 1-10
Author(s):  
JaeWook Shin ◽  
Hyun Jae Baek ◽  
Bum Yong Park ◽  
Jaegeol Cho
Keyword(s):  
Computational Complexity ◽  
Adaptive Filter ◽  
Variable Step Size ◽  
Step Size ◽  
Deviation Analysis ◽  
Sequential Selection ◽  
Variable Step ◽  
Subband Adaptive Filter ◽  
The Mean ◽  
Normalized Subband Adaptive Filter

This letter proposes a sequential selection normalized subband adaptive filter (SS-NSAF) in order to reduce the computational complexity. In addition, a variable step-size algorithm is also proposed using the mean-square deviation analysis of the SS-NSAF. To enhance the performance in terms of the convergence speed, we propose an improved variable step-size SS-NSAF using a two-stage concept. The simulation results show the low computational complexity and low misalignment errors using the proposed algorithm.

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