scholarly journals Robust Adaptive Algorithm by an Adaptive Zero Attractor Controller of ZA-LMS Algorithm

2016 ◽  
Vol 2016 ◽  
pp. 1-7 ◽  
Author(s):  
Radhika Sivashanmugam ◽  
Sivabalan Arumugam
Keyword(s):  
Lms Algorithm ◽  
Mean Square ◽  
Least Mean Square ◽  
Sparse Systems ◽  
Robust Adaptive ◽  
Zero Attracting ◽  
Sparsity Level ◽  
Zero Attractor ◽  
Zero Attractor Controller ◽  
Better Than

This paper proposes a new approach to identify time varying sparse systems. The proposed approach uses Zero-Attracting Least Mean Square (ZA-LMS) algorithm with an adaptive optimal zero attractor controller which can adapt dynamically to the sparseness level and provide appreciable performance in all environments ranging from sparse to nonsparse conditions. The optimal zero attractor controller is derived based on the criterion that confirms largest decrease in mean square deviation (MSD) error. A simple update rule is also proposed to change the zero attractor controller based on the level of sparsity. It is found that, for nonsparse system, the proposed approach converges to LMS (as ZA-LMS cannot outperform LMS when the system is nonsparse) and, for highly sparse system, as the proposed approach is based on optimal zero attractor controller, it converges either similar to ZA-LMS or even better than ZA-LMS (depending on the value of zero attractor controller chosen for ZA-LMS algorithm). The performance of the proposed algorithm is better than ZA-LMS and LMS when the system is semisparse. Simulations were performed to prove that the proposed algorithm is robust against variable sparsity level.

scholarly journals Proposed Hybrid Sparse Adaptive Algorithms for System Identification

2017 ◽  
Vol 13 (2) ◽  
Author(s):  
Mahmood A. K Abdulsattar ◽  
Samer Hussein Ali
Keyword(s):  
System Identification ◽  
Lms Algorithm ◽  
Complete Analysis ◽  
Mean Square ◽  
Mean Square Deviation ◽  
Least Mean Square ◽  
Sparse System ◽  
Wide Range ◽  
Sparse System Identification ◽  
Zero Attracting

Abstract  For sparse system identification,recent suggested algorithms are  -norm Least Mean Square (  -LMS), Zero-Attracting LMS (ZA-LMS), Reweighted Zero-Attracting LMS (RZA-LMS), and p-norm LMS (p-LMS) algorithms, that have modified the cost function of the conventional LMS algorithm by adding a constraint of coefficients sparsity. And so, the proposed algorithms are named  -ZA-LMS,  -RZA-LMS, p-ZA-LMS and p-RZA-LMS that are designed by merging twoconstraints from previous algorithms to improve theconvergence rate and steady state of MSD for sparse system. In this paper, a complete analysis was done for the theoretical operation of proposed algorithms by exited white Gaussian sequence for input signal. The discussion of mean square deviation (MSD) with regard to parameters of algorithms and system sparsity was observed. In addition, in this paper, the correlation between proposed algorithms and the last recent algorithms were presented and the necessary conditions of these proposed algorithms were planned to improve convergence rate. Finally, the results of simulations are compared with theoretical study (?), which is presented to match closely by a wide-range of parameters.. Keywords: Adaptive filter,  -LMS, zero-attracting, p-LMS, mean square deviation, Sparse system identification.

scholarly journals VLSI Systolic Architecture Implementation for Noise Elimination from ECG Signal

2020 ◽  
pp. 415-418
Author(s):  
Vishnusaravanabharathi K ◽  
Dhanasekar J ◽  
Teresa V V ◽  
Boobathi Selvaraj
Keyword(s):  
Lms Algorithm ◽  
Mean Square ◽  
Least Mean Square ◽  
Noise Elimination ◽  
Systolic Architecture ◽  
Ecg Signals ◽  
Separation Technology ◽  
Electrocardiogram Ecg ◽  
Improve Device ◽  
Zero Attractor

Different forms of noise are caused by electrocardiogram (ECG) signals, which vary founded on frequency content. To enhance accurateness and dependability, the elimination of such a trouble is necessary. Denoising ECG pointers is difficult as it is difficult to add secure coefficient filter. It is possible to use adaptive filtering techniques, in which the feature vectors can be changed to top dynamic signal changes. With a degree of sparsity, such as non-sparse, partial sparse and sparse, the framework shifts. The Least Mean Square (LMS) and Zero Attractor LMS (ZA-LMS) convex filtering combination is ideal for both Sparse and Non-Sparse settings. Popular the proposed design, the Systolic Architecture is introduced in direction to improve device efficiency and to reduce the combinational delay path. Systolic architectures are developed using the Xilinx device generator tool for normal Least Mean Square (LMS), Zero Attractor LMS (ZA-LMS) and Convex combinations of Least Mean Square (LMS) and Zero Attractor LMS (ZA-LMS) interfaces.Simulation remains performed with various ECG signals obtained from MIT-BIH database as input to designed filtering and its SNR is obtained. The study shows that the SNR value in systolic architectures is higher than in filter bank structures. For systolic LMS buffers, the SNR value is 4.5 percent greater than the structure of the Lms algorithm. The SNR for the systolic separation technology of ZA-LMS is 2.5 percent higher than the separation technology of ZA-LMS. The SNR value for LMS and ZA-LMS filtering structure systolic convex combinations is 6% higher than that for LMS and ZA-LMS filtering structure convex combinations.

scholarly journals An Effective Adaptive Combination Strategy for Distributed Learning Network

2021 ◽  
Vol 11 (12) ◽  
pp. 5723
Author(s):  
Chundong Xu ◽  
Qinglin Li ◽  
Dongwen Ying
Keyword(s):  
Minimum Variance ◽  
Iteration Scheme ◽  
Estimation Problem ◽  
Lms Algorithm ◽  
Unbiased Estimation ◽  
Mean Square ◽  
Least Mean Square ◽  
Combination Strategy ◽  
Adaptive Combination ◽  
Diffusion Networks

In this paper, we develop a modified adaptive combination strategy for the distributed estimation problem over diffusion networks. We still consider the online adaptive combiners estimation problem from the perspective of minimum variance unbiased estimation. In contrast with the classic adaptive combination strategy which exploits orthogonal projection technology, we formulate a non-constrained mean-square deviation (MSD) cost function by introducing Lagrange multipliers. Based on the Karush–Kuhn–Tucker (KKT) conditions, we derive the fixed-point iteration scheme of adaptive combiners. Illustrative simulations validate the improved transient and steady-state performance of the diffusion least-mean-square LMS algorithm incorporated with the proposed adaptive combination strategy.

Convergence analysis of Bessel beamformer and its comparison with LMS in adaptive array system

2015 ◽  
Vol 34 (3) ◽  
pp. 952-961 ◽  
Author(s):  
M. Yasin ◽  
Pervez Akhtar
Keyword(s):  
Theoretical Model ◽  
Convergence Analysis ◽  
Digital Signal Processor ◽  
Wiener Filter ◽  
Digital Signal ◽  
Lms Algorithm ◽  
Mean Square ◽  
Least Mean Square ◽  
Content Type ◽  
Convergence Performance

Purpose – The purpose of this paper is to analyze the convergence performance of Bessel beamformer, based on the design steps of least mean square (LMS) algorithm, can be named as Bessel LMS (BLMS) algorithm. Its performance is compared in adaptive environment with LMS in terms of two important performance parameters, namely; convergence and mean square error. The proposed BLMS algorithm is implemented on digital signal processor along with antenna array to make it smart in wireless sensor networks. Design/methodology/approach – Convergence analysis is theoretically developed and verified through MatLab Software. Findings – Theoretical model is verified through simulation and its results are shown in the provided table. Originality/value – The theoretical model can obtain validation from well-known result of Wiener filter theory through principle of orthogonality.

An Improved ZA-LMS Algorithm for Sparse System Identification with Variable Sparsity

2014 ◽  
Vol 602-605 ◽  
pp. 2415-2419 ◽  
Author(s):  
Hui Luo ◽  
Yun Lin ◽  
Qing Xia
Keyword(s):  
System Identification ◽  
Impulse Response ◽  
Lms Algorithm ◽  
Mean Square ◽  
Least Mean Square ◽  
Sparse System ◽  
Sparse System Identification ◽  
Least Mean Square Algorithm ◽  
Improved Algorithm

The standard least mean square algorithm does not consider the sparsity of the impulse response,and the performs of the ZA-LMS algorithm deteriorates ,as the degree of system sparsity reduces or non-sparse . Concerning this issue ,the ZA-LMS algorithm is studied and modified in this paper to improve the performance of sparse system identification .The improved algorithm by modify the zero attraction term, which attracts the coefficients only in a certain range (the “inactive” taps), thus have a good performance when the sparsity decreases. The simulations demonstrate that the proposed algorithm significantly outperforms then the ZA-LMS with variable sparisity.

scholarly journals SPEECH RECOGNITION USING SOM AND ACTUATION VIA NETWORK IN MATLAB

2014 ◽  
pp. 191-194
Author(s):  
A. SUBASH CHANDAR ◽  
S. SURIYANARAYANAN ◽  
M. MANIKANDAN
Keyword(s):  
Speech Recognition ◽  
Real Time ◽  
Acoustic Noise ◽  
Lms Algorithm ◽  
Prototype System ◽  
Mean Square ◽  
Least Mean Square ◽  
Self Organizing Maps ◽  
Voice Signal ◽  
The Voice

This paper proposes a method of Speech recognition using Self Organizing Maps (SOM) and actuation through network in Matlab. The different words spoken by the user at client end are captured and filtered using Least Mean Square (LMS) algorithm to remove the acoustic noise. FFT is taken for the filtered voice signal. The voice spectrum is recognized using trained SOM and appropriate label is sent to server PC. The client and the server communication are established using User Datagram Protocol (UDP). Microcontroller (AT89S52) is used to control the speed of the actuator depending upon the input it receives from the client. Real-time working of the prototype system has been verified with successful speech recognition, transmission, reception and actuation via network.

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