ANALYSIS OF THE TRANSFORM DOMAIN LMS ALGORITHM WITH INSUFFICIENT LENGTH ADAPTIVE FILTER

2005 ◽  
Vol 14 (03) ◽  
pp. 469-481 ◽  
Author(s):  
K. MAYYAS
Keyword(s):  
Adaptive Filter ◽  
Adaptive Algorithm ◽  
Lms Algorithm ◽  
Response Analysis ◽  
Transform Domain ◽  
Practical Applications ◽  
Mean Square Convergence ◽  
Gaussian Input ◽  
Unknown System ◽  
Theoretical Results

Though, in most practical applications, the length of the adaptive filter is less than that of the unknown system impulse response, analysis of adaptive filtering algorithms almost always assumed a sufficient length adaptive filter whose length is equal to that of unknown system. Theoretical results on the sufficient length adaptive algorithm do not necessarily apply to the realistic insufficient length case and, therefore, it becomes extremely desirable for practical purposes that we quantify the statistical behavior of the insufficient length adaptive algorithm. In this paper, we analyze the popular Transform Domain LMS (TDLMS) algorithm with insufficient length adaptive filter for Gaussian input data and using the common independence assumption. Analysis yields exact theoretical expressions that describe the mean and mean-square convergence of the algorithm, which lead to a better understanding to the performance properties of the insufficient length TDLMS adaptive algorithm. Simulation experiments illustrate the accuracy of the theoretical results in predicting the convergence behavior of the algorithm.

The Design of a Wavelet-Based Neural Network Adaptive Filter

2013 ◽  
Vol 860-863 ◽  
pp. 2791-2795
Author(s):  
Qian Xiao ◽  
Yu Shan Jiang ◽  
Ru Zheng Cui
Keyword(s):  
Neural Network ◽  
Wavelet Transform ◽  
Real Time ◽  
Adaptive Filter ◽  
Adaptive Algorithm ◽  
Distributed Storage ◽  
Hopfield Neural Network ◽  
Lms Algorithm ◽  
The Neural Network ◽  
Simulation Results

Aiming at the large calculation workload of adaptive algorithm in adaptive filter based on wavelet transform, affecting the filtering speed, a wavelet-based neural network adaptive filter is constructed in this paper. Since the neural network has the ability of distributed storage and fast self-evolution, use Hopfield neural network to implement adaptive filter LMS algorithm in this filter so as to improve the speed of operation. The simulation results prove that, the new filter can achieve rapid real-time denoising.

Implementation of the Hartley-Transform-Based Block LMS Algorithm

1993 ◽  
Author(s):  
Tokunbo Ogunfunmi
Keyword(s):  
Digital Signal Processor ◽  
Adaptive Algorithm ◽  
Interference Cancellation ◽  
Digital Signal ◽  
Cost Effective ◽  
Lms Algorithm ◽  
Transform Domain ◽  
Least Mean Square ◽  
Hartley Transform ◽  
The Time Domain

Abstract This paper presents a cost-effective The Frequency-domain Least-Mean-Square (FLMS) adaptive algorithm (or more generally the Transform-domain LMS adaptive algorithm) [12], [13] has mainly two advantages over the conventional LMS algorithm [19]. The first is that it overcomes the slow convergence of the LMS algorithm by orthogonalizing the input (thereby performing better than the LMS for correlated input signals) and the second advantage is that it can be used for implementing the time-domain Block LMS (BLMS) algorithms as well [18]. The Hartley transform is a newly introduced real-to-real transform that is a suitable replacement for the complex Fourier transform [1] and [2] in several adaptive filtering applications such as adaptive interference cancellation that has wide applicability to problems in telecommunications, biomedical engineering, etc. The realization of the Transform-domain BLMS adaptive algorithm based on the Discrete Hartley Transforms (DHT) and its implementation on the TMS320C30 digital signal processor chip is described.

scholarly journals System Identification using Adaptive Filters

2020 ◽  
Vol 9 (9) ◽  
pp. 409-414
Keyword(s):  
System Identification ◽  
Adaptive Filter ◽  
Adaptive Filters ◽  
Lms Algorithm ◽  
Mean Square ◽  
Least Mean Square ◽  
Iir Filter ◽  
Unknown System

Identification of system is one of the major applications of an adaptive filters, mainly Least Mean Square (LMS) algorithm, because of its ease in calculations, the ability to withstand or overcome any conditions. The unknown System can be a FIR or an IIR filter. Same input is fed into both undefined system (which is unknown to us) and the adaptive filter, their outputs will be subtracted and the error subtracted signal will be given to adaptive filter. The adaptive filter is modified until the system which is unknown and the adaptive filter becomes relatively equal. System identification defines the type and functionality of the system. By utilizing the weights, the output of the system for any input can be predicted.

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.

Adaptive filter design based on the LMS algorithm for delay elimination in TCR/FC compensators

2011 ◽  
Vol 50 (2) ◽  
pp. 142-149 ◽  
Author(s):  
Rahmat Allah Hooshmand ◽  
Mahdi Torabian Esfahani
Keyword(s):  
Adaptive Filter ◽  
Filter Design ◽  
Lms Algorithm

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.

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