Mean square convergence analysis of LMS algorithm

1990 ◽  
Vol 26 (20) ◽  
pp. 1705 ◽  
Author(s):  
V.A. Gholkar
Keyword(s):  
Convergence Analysis ◽  
Lms Algorithm ◽  
Mean Square ◽  
Mean Square Convergence

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.

Modifier formula on mean square convergence of LMS algorithm

2002 ◽  
Vol 38 (19) ◽  
pp. 1147 ◽  
Author(s):  
Yuantao Gu ◽  
Kun Tang ◽  
Huijuan Cui ◽  
Wen Du
Keyword(s):  
Lms Algorithm ◽  
Mean Square ◽  
Mean Square Convergence

scholarly journals Convergence Analysis for the SMC-MeMBer and SMC-CBMeMBer Filters

2012 ◽  
Vol 2012 ◽  
pp. 1-25 ◽  
Author(s):  
Feng Lian ◽  
Chen Li ◽  
Chongzhao Han ◽  
Hui Chen
Keyword(s):  
Monte Carlo ◽  
Convergence Analysis ◽  
Sequential Monte Carlo ◽  
Mean Square ◽  
Convergence Results ◽  
Mean Square Convergence ◽  
The Mean ◽  
Mean Square Errors

The convergence for the sequential Monte Carlo (SMC) implementations of the multitarget multi-Bernoulli (MeMBer) filter and cardinality-balanced MeMBer (CBMeMBer) filters is studied here. This paper proves that the SMC-MeMBer and SMC-CBMeMBer filters, respectively, converge to the true MeMBer and CBMeMBer filters in the mean-square sense and the corresponding bounds for the mean-square errors are given. The significance of this paper is in theory to present the convergence results of the SMC-MeMBer and SMC-CBMeMBer filters and the conditions under which the two filters satisfy mean-square convergence.

Quantized kernel maximum correntropy and its mean square convergence analysis

2017 ◽  
Vol 63 ◽  
pp. 164-176 ◽  
Author(s):  
Shiyuan Wang ◽  
Yunfei Zheng ◽  
Shukai Duan ◽  
Lidan Wang ◽  
Hongtao Tan
Keyword(s):  
Convergence Analysis ◽  
Mean Square ◽  
Mean Square Convergence

Mean square convergence analysis for kernel least mean square algorithm

2012 ◽  
Vol 92 (11) ◽  
pp. 2624-2632 ◽  
Author(s):  
Badong Chen ◽  
Songlin Zhao ◽  
Pingping Zhu ◽  
José C. Príncipe
Keyword(s):  
Convergence Analysis ◽  
Mean Square ◽  
Least Mean Square ◽  
Mean Square Convergence ◽  
Least Mean Square Algorithm ◽  
Kernel Least Mean Square

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.

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