Failure time analysis for LMS algorithms

Zerai, A.A.; Bucklew, J.A.

doi:10.1109/18.992781

Cited by 2 publications

(2 citation statements)

References 9 publications

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“…The failure time analysis for LMS algorithms shown by Abel A et al [61]. A system failure may depend upon the occurrence of a large error and clumps of large errors.…”

Section: Variants Of Adaptive Algorithmsmentioning

confidence: 99%

A Survey with Emphasis on Adaptive filter, Structure, LMS and NLMS Adaptive Algorithm for Adaptive Noise Cancellation System

Nagal¹,

Kumar²,

Poonam³

2017

JESTR

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This paper discusses the evolution of adaptive filtering, filter structure, adaptive algorithms used for noise cancellation over the past five decades. The field of adaptive signal processing has been matter of research for over 50-60 years. The major growth occurred in this field in eighties because of the availability of implementation tools and textbooks. Adaptive signal processing has made a significant contribution in the last 50 years. The applications of adaptive signal processing are very appealing because of its properties like low costing, constancy, fidelity, small sizes, and adjustability. This revolutionary change brought along with the problems of noise and the solution is the design of the adaptive filter. This paper mainly focused on adaptive filter, and its structure, the Least Mean Square Algorithm (LMS) and Normalized Least Mean Square Algorithm (NLMS), used for noise cancellation. This paper could serve as a survey for beginners and as a reference to select the related reference of their field.

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“…The failure time analysis for LMS algorithms shown by Abel A et al [61]. A system failure may depend upon the occurrence of a large error and clumps of large errors.…”

Section: Variants Of Adaptive Algorithmsmentioning

confidence: 99%

A Survey with Emphasis on Adaptive filter, Structure, LMS and NLMS Adaptive Algorithm for Adaptive Noise Cancellation System

Nagal¹,

Kumar²,

Poonam³

2017

JESTR

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“…It is vulnerable to ill conditioning or inadequate excitation of the input. The incremental adaptive solution using LMS (ILMS) algorithm [3] suffers from drift problem in non ideal situations [9]. Incremental mode of cooperation is considered in this paper, where each node communicates with its neighbor in a cyclic manner.…”

Section: Introductionmentioning

confidence: 99%

Distributed Incremental Leaky LMS

Sowjanya

Sahoo

Kumar

2015

2015 International Conference on Communications and Signal Processing (ICCSP)

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Adaptive algorithms are applied to distributed networks to endow the network with adaptation capabilities. Incremental Strategy is the simplest mode of cooperation as it needs less amount of communication between the nodes. The adaptive incremental strategy which is developed for distributed networks using LMS algorithm, suffers from drift problem, where the parameter estimate will go unbounded in non ideal or practical implementations. Drift problem or divergence of the parameter estimate occurs due to continuous accumulation of quantization errors, finite precision errors and insufficient spectral excitation or ill conditioning of input sequence. They result in overflow and near singular auto correlation matrix, which provokes slow escape of parameter estimate to go unbound. The proposed method uses the Leaky LMS algorithm, which introduces a leakage factor in the update equation, and so prevents the weights to go unbounded by leaking energy out.

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Failure time analysis for LMS algorithms

Cited by 2 publications

References 9 publications

A Survey with Emphasis on Adaptive filter, Structure, LMS and NLMS Adaptive Algorithm for Adaptive Noise Cancellation System

A Survey with Emphasis on Adaptive filter, Structure, LMS and NLMS Adaptive Algorithm for Adaptive Noise Cancellation System

Distributed Incremental Leaky LMS

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