A robust variable step size fractional least mean square (RVSS-FLMS) algorithm

Shujaat Khan, Muhammad Usman, Imran Naseem, Roberto Togneri, Mohammed Bennamoun

    Research output: Chapter in Book/Conference paperConference paper

    4 Citations (Scopus)

    Abstract

    In this paper, we propose an adaptive framework for the variable step size of the fractional least mean square (FLMS) algorithm. The proposed algorithm named the robust variable step size-FLMS (RVSS-FLMS), dynamically updates the step size of the FLMS to achieve high convergence rate with low steady state error. For the evaluation purpose, the problem of system identification is considered. The experiments clearly show that the proposed approach achieves better convergence rate compared to the FLMS and adaptive step-size modified FLMS (AMFLMS).

    Original languageEnglish
    Title of host publicationProceedings - 2017 IEEE 13th International Colloquium on Signal Processing and its Applications, CSPA 2017
    EditorsMohd Nasir Taib
    PublisherIEEE, Institute of Electrical and Electronics Engineers
    Pages1-6
    Number of pages6
    ISBN (Electronic)9781509011841
    DOIs
    Publication statusPublished - 2017
    Event13th IEEE International Colloquium on Signal Processing and its Applications, CSPA 2017 - Penang, Malaysia
    Duration: 10 Mar 201712 Mar 2017

    Conference

    Conference13th IEEE International Colloquium on Signal Processing and its Applications, CSPA 2017
    CountryMalaysia
    CityPenang
    Period10/03/1712/03/17

    Fingerprint

    Identification (control systems)
    system identification
    Experiments
    evaluation

    Cite this

    Khan, S., Usman, M., Naseem, I., Togneri, R., & Bennamoun, M. (2017). A robust variable step size fractional least mean square (RVSS-FLMS) algorithm. In M. N. Taib (Ed.), Proceedings - 2017 IEEE 13th International Colloquium on Signal Processing and its Applications, CSPA 2017 (pp. 1-6). [8064914] IEEE, Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/CSPA.2017.8064914
    Khan, Shujaat ; Usman, Muhammad ; Naseem, Imran ; Togneri, Roberto ; Bennamoun, Mohammed. / A robust variable step size fractional least mean square (RVSS-FLMS) algorithm. Proceedings - 2017 IEEE 13th International Colloquium on Signal Processing and its Applications, CSPA 2017. editor / Mohd Nasir Taib. IEEE, Institute of Electrical and Electronics Engineers, 2017. pp. 1-6
    @inproceedings{9b09ef42bc1f4a9d880381e6d909cb19,
    title = "A robust variable step size fractional least mean square (RVSS-FLMS) algorithm",
    abstract = "In this paper, we propose an adaptive framework for the variable step size of the fractional least mean square (FLMS) algorithm. The proposed algorithm named the robust variable step size-FLMS (RVSS-FLMS), dynamically updates the step size of the FLMS to achieve high convergence rate with low steady state error. For the evaluation purpose, the problem of system identification is considered. The experiments clearly show that the proposed approach achieves better convergence rate compared to the FLMS and adaptive step-size modified FLMS (AMFLMS).",
    keywords = "adaptive filter, adaptive step-size modified fractional LMS (AMFLMS), channel equalization, fractional calculus, fractional LMS (FLMS), high convergence, Least mean square (LMS), low steady state error, modified fractional LMS (MFLMS), plant identification, robust variable step size (RVSS), robust variable step size FLMS (RVSS-FLMS)",
    author = "Shujaat Khan and Muhammad Usman and Imran Naseem and Roberto Togneri and Mohammed Bennamoun",
    year = "2017",
    doi = "10.1109/CSPA.2017.8064914",
    language = "English",
    pages = "1--6",
    editor = "Taib, {Mohd Nasir }",
    booktitle = "Proceedings - 2017 IEEE 13th International Colloquium on Signal Processing and its Applications, CSPA 2017",
    publisher = "IEEE, Institute of Electrical and Electronics Engineers",
    address = "United States",

    }

    Khan, S, Usman, M, Naseem, I, Togneri, R & Bennamoun, M 2017, A robust variable step size fractional least mean square (RVSS-FLMS) algorithm. in MN Taib (ed.), Proceedings - 2017 IEEE 13th International Colloquium on Signal Processing and its Applications, CSPA 2017., 8064914, IEEE, Institute of Electrical and Electronics Engineers, pp. 1-6, 13th IEEE International Colloquium on Signal Processing and its Applications, CSPA 2017, Penang, Malaysia, 10/03/17. https://doi.org/10.1109/CSPA.2017.8064914

    A robust variable step size fractional least mean square (RVSS-FLMS) algorithm. / Khan, Shujaat; Usman, Muhammad; Naseem, Imran; Togneri, Roberto; Bennamoun, Mohammed.

    Proceedings - 2017 IEEE 13th International Colloquium on Signal Processing and its Applications, CSPA 2017. ed. / Mohd Nasir Taib. IEEE, Institute of Electrical and Electronics Engineers, 2017. p. 1-6 8064914.

    Research output: Chapter in Book/Conference paperConference paper

    TY - GEN

    T1 - A robust variable step size fractional least mean square (RVSS-FLMS) algorithm

    AU - Khan, Shujaat

    AU - Usman, Muhammad

    AU - Naseem, Imran

    AU - Togneri, Roberto

    AU - Bennamoun, Mohammed

    PY - 2017

    Y1 - 2017

    N2 - In this paper, we propose an adaptive framework for the variable step size of the fractional least mean square (FLMS) algorithm. The proposed algorithm named the robust variable step size-FLMS (RVSS-FLMS), dynamically updates the step size of the FLMS to achieve high convergence rate with low steady state error. For the evaluation purpose, the problem of system identification is considered. The experiments clearly show that the proposed approach achieves better convergence rate compared to the FLMS and adaptive step-size modified FLMS (AMFLMS).

    AB - In this paper, we propose an adaptive framework for the variable step size of the fractional least mean square (FLMS) algorithm. The proposed algorithm named the robust variable step size-FLMS (RVSS-FLMS), dynamically updates the step size of the FLMS to achieve high convergence rate with low steady state error. For the evaluation purpose, the problem of system identification is considered. The experiments clearly show that the proposed approach achieves better convergence rate compared to the FLMS and adaptive step-size modified FLMS (AMFLMS).

    KW - adaptive filter

    KW - adaptive step-size modified fractional LMS (AMFLMS)

    KW - channel equalization

    KW - fractional calculus

    KW - fractional LMS (FLMS)

    KW - high convergence

    KW - Least mean square (LMS)

    KW - low steady state error

    KW - modified fractional LMS (MFLMS)

    KW - plant identification

    KW - robust variable step size (RVSS)

    KW - robust variable step size FLMS (RVSS-FLMS)

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    U2 - 10.1109/CSPA.2017.8064914

    DO - 10.1109/CSPA.2017.8064914

    M3 - Conference paper

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    EP - 6

    BT - Proceedings - 2017 IEEE 13th International Colloquium on Signal Processing and its Applications, CSPA 2017

    A2 - Taib, Mohd Nasir

    PB - IEEE, Institute of Electrical and Electronics Engineers

    ER -

    Khan S, Usman M, Naseem I, Togneri R, Bennamoun M. A robust variable step size fractional least mean square (RVSS-FLMS) algorithm. In Taib MN, editor, Proceedings - 2017 IEEE 13th International Colloquium on Signal Processing and its Applications, CSPA 2017. IEEE, Institute of Electrical and Electronics Engineers. 2017. p. 1-6. 8064914 https://doi.org/10.1109/CSPA.2017.8064914