The leaky least mean mixed norm algorithm

Nasar, Mohammed Abdul; Zerguine, Azzedine

doi:10.1109/acssc.2013.6810550

Cited by 15 publications

(4 citation statements)

References 23 publications

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“…As for LMF algorithm, the cost function is applied the fourthorder power optimization rather than square power of LMS. The GA-LMF updating rule is motivated by minimizing the cost function in (20). Therefore, the instantaneous cost function J(i) of GA-LMF at instant i can be obtained by rewriting the LMF cost function in the GA form [42], [43].…”

Section: Adaptive Filtering Algorithms Based On Higher-order Stamentioning

confidence: 99%

“…LMMN performs better than LMS and LMF. Nasar and Zerguine [20] propose a leaky LMMN algorithm to prevent instable weight drifting. Although the LMMN algorithm provides a better performance under Gaussian and Non-Gaussian environments than LMS and LMF algorithms, its performance will still decline due to impulsive interferences which exist in practical environment [21].…”

Section: Introductionmentioning

confidence: 99%

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Novel Adaptive Filtering Algorithms Based on Higher-Order Statistics and Geometric Algebra

Wang

et al. 2020

IEEE Access

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Adaptive filtering algorithms based on higher-order statistics are proposed for multidimensional signal processing in geometric algebra (GA) space. In this paper, the proposed adaptive filtering algorithms utilize the advantage of GA theory in multi-dimensional signal processing to represent a multidimensional signal as a GA multivector. In addition, the original least-mean fourth (LMF) and least-mean mixed-norm (LMMN) adaptive filtering algorithms are extended to GA space for multi-dimensional signal processing. Both the proposed GA-based least-mean fourth (GA-LMF) and GA-based least-mean mixednorm (GA-LMMN) algorithms need to minimize cost functions based on higher-order statistics of the error signal in GA space. The simulation results show that the proposed GA-LMF algorithm performs better in terms of convergence rate and steady-state error under a much smaller step size. The proposed GA-LMMN algorithm makes up for the instability of GA-LMF as the step size increases, and its performance is more stable in mean absolute error and convergence rate. INDEX TERMS Adaptive filters, geometric algebra, least-mean fourth, least-mean mixed-norm.

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Section: Adaptive Filtering Algorithms Based On Higher-order Stamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Novel Adaptive Filtering Algorithms Based on Higher-Order Statistics and Geometric Algebra

Wang

et al. 2020

IEEE Access

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“…The Leaky Least Mean Mixed Norm (LLMMN) algorithm is a mixture of the LLMS and LLMF algorithms. The transient analysis of the LLMMN algorithm was performed [11]. Additionally, the step size upper bounds of the LLMMN algorithm was derived [11].…”

Section: Introductionmentioning

confidence: 99%

“…The transient analysis of the LLMMN algorithm was performed [11]. Additionally, the step size upper bounds of the LLMMN algorithm was derived [11]. A new fixed point Leaky Sign Regressor Least Mean Square (LSRLMS) powered noise cancellation technique was proposed for eliminating the 60-Hz PLI noise embedded in the ECG signal [12].…”

Section: Introductionmentioning

confidence: 99%

A New Fixed Point Noise Cancellation Method for Suppressing Power Line Interference in Electrocardiogram Signals

Faiz

Reni

Kale

2022

2022 E-Health and Bioengineering Conference (EHB)

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In this article, a new fixed point Leaky Sign Regressor Least Mean Mixed Norm (LSRLMMN) powered adaptive noise cancellation technique is being used for eliminating the Power Line Interference (PLI) noise embedded in the ElectroCardioGram (ECG) signal. The fixed point LSRLMMN powered noise cancellation technique used in this article has been completely quantized. The intention for the extensive quantization study and modeling approach was with a view to the physical integrated circuit implementation. All the modeling and simulation studies were carried out at the bit-level with various loss of precision schemes to ensure compliance with the set specification. The filter coefficients and all the data paths are quantized in order to establish at a high-level behavioral level of the parameters for a decreased complexity in integrated circuit implementation.

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Performance Analysis for the Least Mean Mixed Norm Algorithm with a Leaky Factor

Nasar,

Midhunchakkaravarthy,

Rehman

2024

Lecture Notes in Electrical Engineering

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The leaky least mean mixed norm algorithm

Cited by 15 publications

References 23 publications

Novel Adaptive Filtering Algorithms Based on Higher-Order Statistics and Geometric Algebra

Novel Adaptive Filtering Algorithms Based on Higher-Order Statistics and Geometric Algebra

A New Fixed Point Noise Cancellation Method for Suppressing Power Line Interference in Electrocardiogram Signals

Performance Analysis for the Least Mean Mixed Norm Algorithm with a Leaky Factor

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