Efficient Recursive Total Least Mean Fourth Algorithm

Kabiru N. Aliyu; Mohamed Hafez Mohamed; Abdulmajid Lawal; Ali Muqaibel; Muhammad Moinuddin; Azzedine Zerguine

doi:10.1109/SSD58187.2023.10411306

Efficient Recursive Total Least Mean Fourth Algorithm

Kabiru N. Aliyu^*, Mohamed Hafez Mohamed, Abdulmajid Lawal, Ali Muqaibel, Muhammad Moinuddin, Azzedine Zerguine

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

In this work, an iterative total least mean fourth (TLMF) algorithm is devised to solve adaptive filtering problems when both the input and output signals are corrupted with noise. The proposed algorithm is based on a stochastic approach related to the existing total least mean square (TLMS) algorithm. The cost function of the TLMF is defined in terms of the fourth power of the error and minimized iteratively to reach the optimal weight solution. The unknown system is evaluated at various levels of signal-to-noise ratio (SNR). The simulation results showed that the proposed algorithm produced interesting results when both the noise is white or coloured.

Original language	English
Title of host publication	2023 20th International Multi-Conference on Systems, Signals and Devices, SSD 2023
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	784-787
Number of pages	4
ISBN (Electronic)	9798350332568
DOIs	https://doi.org/10.1109/SSD58187.2023.10411306
State	Published - 2023
Event	20th International Multi-Conference on Systems, Signals and Devices, SSD 2023 - Mahdia, Tunisia Duration: 20 Feb 2023 → 23 Feb 2023

Publication series

Name	2023 20th International Multi-Conference on Systems, Signals and Devices, SSD 2023

Conference

Conference	20th International Multi-Conference on Systems, Signals and Devices, SSD 2023
Country/Territory	Tunisia
City	Mahdia
Period	20/02/23 → 23/02/23

Bibliographical note

Publisher Copyright:
© 2023 IEEE.

Keywords

Adaptive filtering
cost function
least mean squares
total least mean fourth
total least mean square

ASJC Scopus subject areas

Artificial Intelligence
Computer Science Applications
Computer Networks and Communications
Information Systems
Signal Processing
Health Informatics
Instrumentation

Access to Document

10.1109/SSD58187.2023.10411306

Cite this

Aliyu, K. N., Mohamed, M. H., Lawal, A., Muqaibel, A., Moinuddin, M., & Zerguine, A. (2023). Efficient Recursive Total Least Mean Fourth Algorithm. In 2023 20th International Multi-Conference on Systems, Signals and Devices, SSD 2023 (pp. 784-787). (2023 20th International Multi-Conference on Systems, Signals and Devices, SSD 2023). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/SSD58187.2023.10411306

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title = "Efficient Recursive Total Least Mean Fourth Algorithm",

abstract = "In this work, an iterative total least mean fourth (TLMF) algorithm is devised to solve adaptive filtering problems when both the input and output signals are corrupted with noise. The proposed algorithm is based on a stochastic approach related to the existing total least mean square (TLMS) algorithm. The cost function of the TLMF is defined in terms of the fourth power of the error and minimized iteratively to reach the optimal weight solution. The unknown system is evaluated at various levels of signal-to-noise ratio (SNR). The simulation results showed that the proposed algorithm produced interesting results when both the noise is white or coloured.",

keywords = "Adaptive filtering, cost function, least mean squares, total least mean fourth, total least mean square",

author = "Aliyu, {Kabiru N.} and Mohamed, {Mohamed Hafez} and Abdulmajid Lawal and Ali Muqaibel and Muhammad Moinuddin and Azzedine Zerguine",

note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 20th International Multi-Conference on Systems, Signals and Devices, SSD 2023 ; Conference date: 20-02-2023 Through 23-02-2023",

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doi = "10.1109/SSD58187.2023.10411306",

language = "English",

series = "2023 20th International Multi-Conference on Systems, Signals and Devices, SSD 2023",

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Aliyu, KN, Mohamed, MH, Lawal, A, Muqaibel, A, Moinuddin, M & Zerguine, A 2023, Efficient Recursive Total Least Mean Fourth Algorithm. in 2023 20th International Multi-Conference on Systems, Signals and Devices, SSD 2023. 2023 20th International Multi-Conference on Systems, Signals and Devices, SSD 2023, Institute of Electrical and Electronics Engineers Inc., pp. 784-787, 20th International Multi-Conference on Systems, Signals and Devices, SSD 2023, Mahdia, Tunisia, 20/02/23. https://doi.org/10.1109/SSD58187.2023.10411306

Efficient Recursive Total Least Mean Fourth Algorithm. / Aliyu, Kabiru N.; Mohamed, Mohamed Hafez; Lawal, Abdulmajid et al.
2023 20th International Multi-Conference on Systems, Signals and Devices, SSD 2023. Institute of Electrical and Electronics Engineers Inc., 2023. p. 784-787 (2023 20th International Multi-Conference on Systems, Signals and Devices, SSD 2023).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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T1 - Efficient Recursive Total Least Mean Fourth Algorithm

AU - Aliyu, Kabiru N.

AU - Mohamed, Mohamed Hafez

AU - Lawal, Abdulmajid

AU - Muqaibel, Ali

AU - Moinuddin, Muhammad

AU - Zerguine, Azzedine

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N2 - In this work, an iterative total least mean fourth (TLMF) algorithm is devised to solve adaptive filtering problems when both the input and output signals are corrupted with noise. The proposed algorithm is based on a stochastic approach related to the existing total least mean square (TLMS) algorithm. The cost function of the TLMF is defined in terms of the fourth power of the error and minimized iteratively to reach the optimal weight solution. The unknown system is evaluated at various levels of signal-to-noise ratio (SNR). The simulation results showed that the proposed algorithm produced interesting results when both the noise is white or coloured.

AB - In this work, an iterative total least mean fourth (TLMF) algorithm is devised to solve adaptive filtering problems when both the input and output signals are corrupted with noise. The proposed algorithm is based on a stochastic approach related to the existing total least mean square (TLMS) algorithm. The cost function of the TLMF is defined in terms of the fourth power of the error and minimized iteratively to reach the optimal weight solution. The unknown system is evaluated at various levels of signal-to-noise ratio (SNR). The simulation results showed that the proposed algorithm produced interesting results when both the noise is white or coloured.

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KW - cost function

KW - least mean squares

KW - total least mean fourth

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BT - 2023 20th International Multi-Conference on Systems, Signals and Devices, SSD 2023

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Aliyu KN, Mohamed MH, Lawal A, Muqaibel A, Moinuddin M, Zerguine A. Efficient Recursive Total Least Mean Fourth Algorithm. In 2023 20th International Multi-Conference on Systems, Signals and Devices, SSD 2023. Institute of Electrical and Electronics Engineers Inc. 2023. p. 784-787. (2023 20th International Multi-Conference on Systems, Signals and Devices, SSD 2023). doi: 10.1109/SSD58187.2023.10411306

Efficient Recursive Total Least Mean Fourth Algorithm

Abstract

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Conference

Bibliographical note

Keywords

ASJC Scopus subject areas

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Cite this