Variable step‐size non‐negative normalised least‐mean‐square‐type algorithm

Jung, Sang Mok; Seo, Jihye; Park, PooGyeon

doi:10.1049/iet-spr.2014.0122

Cited by 4 publications

(2 citation statements)

References 22 publications

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“…Taking partial differentiation of (8) w.r.t. g o and g 1 gives, (10) Rearranging ( 9) and (10) (11) The matrix in (11) is known as Hessian matrix, H. To find the direction vector such that J(w+g) is minimum, (11) From (15) it is observed that SDA requires presence of parameters R and p, but for most of the real time applications these are not known. To address this issue, LMS algorithm is devised.…”

Section: A Newton's Algorithmsmentioning

confidence: 99%

Implementation of LMS algorithm for system identification

Prasad

Patil

2016

2016 International Conference on Signal and Information Processing (IConSIP)

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An adaptive filter is defined as a digital filter that has the capability of self adjusting its transfer function under the control of some optimizing algorithms. Most common optimizing algorithms are Least Mean Square (LMS) and Recursive Least Square (RLS). Although RLS algorithm perform superior to LMS algorithm, it has very high computational complexity so not useful in most of the practical scenario. So most feasible choice of the adaptive filtering algorithm is the LMS algorithm including its various variants. The LMS algorithm uses transversal FIR filter as underlying digital filter. This paper is based on implementation and optimization of LMS algorithm for the application of unknown system identification.

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Section: A Newton's Algorithmsmentioning

confidence: 99%

Implementation of LMS algorithm for system identification

Prasad

Patil

2016

2016 International Conference on Signal and Information Processing (IConSIP)

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“…Upon applying the algorithm to the linear equalization we can study the various aspects, behaviours, advantages, and drawbacks of the technique. Moreover this technique, which is essentially employed in the field of adaptive filters, has many different applications in the fields of communications, computers and adaptive signal processing in general [26][27][28][29][30] due to its computation simplicity [31]. Figure 1 represents the block diagram of the channel equalization.…”

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confidence: 99%

LMS Algorithm for Adaptive Transversal Equalization of a Linear Dispersive Communication Channel

Arslan

2020

Review of Computer Engineering Research

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The presence of any type of distortion in communication system, regardless of the causes, is undesirable and undeniably has a negative impacts on the system in general and therefore it is necessary to eliminate its effects. This study employs one of the wellknown algorithms for adaptive equalization of linear dispersive communication channel which is Least Mean Square (LMS) algorithm. The LMS technique is basically utilized to eliminate the noise in communication channel. The novelty of this paper includes the profoundly analyzing of the influence of rate of convergence, miss-adjustment, computational requirement, and sensitivity to Eigen-value spread in sufficient details in a simple and plain way. Moreover, the system performance improvement employing the feedback equalizer technique is intensively presented which shows that our methodology is very effective to eliminate the noise in the system. The simulation work has been performed with MATLAB software. Contribution/Originality: This study uses new estimation methodology which is regarded as profoundly analyzing of the influence of rate of convergence, miss-adjustment, computational requirement, and sensitivity to Eigen-value spread in sufficient details in a simple and plain way. 1. INTRODUCTION Adaptive filters are used extensively in statistical signal processing and offer a great improvement in performance compared with the conventional fixed filters [1-7].The subject of adaptive filters in general and linear adaptive filters in particular has drawn the attention of many researchers and therefore a various methodologies have been developed and implemented to solve any given problem in the area of statistical signal processing. The linear adaptive filter includes a filter whose function is to produce a desired output, and an adaptive algorithm to set the filter parameters. The selected algorithm is significantly affected by the filter structure which is mainly classified into finite impulse filter (FIR) [8-14] and infinite impulse response (IIR) [15-19]. Generally, adaptive algorithm attempts to minimize the error function in the input, reference, and output signals to near zero value. The most commonly minimization methods used for adaptive filters are Quasi-Newton techniques and the steepest-descent gradient technique [20, 21]. The latter is easy to perform but the quasi-Newton strategy basically has better convergence rate. Therefore the best choice is the Quasi-Newton techniques which have better computational performance and good convergence. But disadvantage of this method is very sensitive to the instability matters. In all these strategies, it is necessary to select the convergence factor carefully based on the specific adaptation issue. The error signal normally is created in different ways but the most popular techniques are Mean Square Error (MSE) methodology, and Least Squares (LS) technique. MSE is requiring an

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A modified non-negative LMS algorithm for online system identification

Khalili

2018

AEU - International Journal of Electronics and Communications

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Variable step‐size non‐negative normalised least‐mean‐square‐type algorithm

Cited by 4 publications

References 22 publications

Implementation of LMS algorithm for system identification

Implementation of LMS algorithm for system identification

LMS Algorithm for Adaptive Transversal Equalization of a Linear Dispersive Communication Channel

A modified non-negative LMS algorithm for online system identification

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