scholarly journals Performance Analysis of Kernel Adaptive Filters based on LMS Algorithm

2013 ◽  
Vol 20 ◽  
pp. 39-45 ◽  
Author(s):  
Ibtissam Constantin ◽  
Régis Lengellé
Keyword(s):  
Performance Analysis ◽  
Adaptive Filters ◽  
Lms Algorithm

scholarly journals Performance Analysis of FrFT based Adaptive Filters with LMS Algorithm

2014 ◽  
Vol 99 (6) ◽  
pp. 15-19
Author(s):  
K. YashasKumar ◽  
P. V. Muralidhar ◽  
D. V. L. N. Sastry
Keyword(s):  
Performance Analysis ◽  
Adaptive Filters ◽  
Lms Algorithm

Adaptive Filters with Codified Error LMS Algorithm

2006 ◽  
Vol 65 (6) ◽  
pp. 567-579 ◽  
Author(s):  
Jose Velazquez-Lopez ◽  
Juan Carlos Sanchez-Garcia ◽  
Hector Manuel Perez-Meana
Keyword(s):  
Adaptive Filters ◽  
Lms Algorithm

scholarly journals Adaptive method to predict and track unknown system behaviors using RLS and LMS algorithms

2021 ◽  
Vol 34 (1) ◽  
pp. 133-140
Author(s):  
Teimour Tajdari
Keyword(s):  
Input Signal ◽  
Output Signal ◽  
Adaptive Filters ◽  
Adaptive Method ◽  
Lms Algorithm ◽  
Recursive Least Squares ◽  
Least Mean Square ◽  
Filter Output ◽  
System Output ◽  
Unknown System

This study investigates the ability of recursive least squares (RLS) and least mean square (LMS) adaptive filtering algorithms to predict and quickly track unknown systems. Tracking unknown system behavior is important if there are other parallel systems that must follow exactly the same behavior at the same time. The adaptive algorithm can correct the filter coefficients according to changes in unknown system parameters to minimize errors between the filter output and the system output for the same input signal. The RLS and LMS algorithms were designed and then examined separately, giving them a similar input signal that was given to the unknown system. The difference between the system output signal and the adaptive filter output signal showed the performance of each filter when identifying an unknown system. The two adaptive filters were able to track the behavior of the system, but each showed certain advantages over the other. The RLS algorithm had the advantage of faster convergence and fewer steady-state errors than the LMS algorithm, but the LMS algorithm had the advantage of less computational complexity.

A Novel Design and FPGA Implementation of Filters Adapted Using LMS Variants

2018 ◽  
Vol 27 (08) ◽  
pp. 1850125
Author(s):  
Sakshi ◽  
Ravi Kumar
Keyword(s):  
Interference Cancellation ◽  
Adaptive Filters ◽  
Hardware Implementation ◽  
Lms Algorithm ◽  
Variable Step Size ◽  
Step Size ◽  
Silicon Area ◽  
Novel Structure ◽  
Wide Range ◽  
Field Programmable

Adaptive filters have wide range of applications in areas such as echo or interference cancellation, prediction and system identification. Due to high computational complexity of adaptive filters, their hardware implementation is not an easy task. However, it becomes essential in many cases where real-time execution is needed. This paper presents the design and hardware implementation of a variable step size 40 order adaptive filter for de-noising acoustic signals. To ensure an area efficient implementation, a novel structure is being proposed. The proposed structure eliminates the requirement of extra registers for storage of delayed inputs thereby reducing the silicon area. The structure is compared with direct-form and transposed-form structures by adapting the filter coefficients using four different variants of the least means square (LMS) algorithm. Subsequently, the filters are implemented on three different field programmable gate arrays (FPGAs) viz. Spartan 6, Virtex 6 and Virtex 7 to find out the best device family that can be used to implement an Adaptive noise canceller (ANC) by comparing speed, power and area utilization. The synthesis results clearly reveal that ANC designed using the proposed structure has resulted in a reduction in silicon area without incurring any significant overhead in terms of power or delay.

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