FCLMS: Fractional complex LMS algorithm for complex system identification

Jawwad Ahmad; Shujaat Khan; Muhammad Usman; Imran Naseem; Muhammad Moinuddin; Hassan Jamil Syed

doi:10.1109/CSPA.2017.8064921

FCLMS: Fractional complex LMS algorithm for complex system identification

Jawwad Ahmad^*
, Shujaat Khan
, Muhammad Usman
, Imran Naseem
, Muhammad Moinuddin
, Hassan Jamil Syed

^*Corresponding author for this work

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

14 Scopus citations

Abstract

In this paper, a fractional order calculus based least mean square algorithm is proposed for complex system identification. The proposed algorithm, named as, fractional complex least mean square (FCLMS), successfully deals with the problem of complex error due to negative weights or complex input/output in the FLMS. For the evaluation purpose a complex linear system is considered. The FCLMS algorithm successfully identifies the complex system and achieve high convergence rate without compromising the steady state error. The convergence rate of the proposed FCLMS is two times better than that of the complex least mean square (CLMS).

Original language	English
Title of host publication	Proceedings - 2017 IEEE 13th International Colloquium on Signal Processing and its Applications, CSPA 2017
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	39-43
Number of pages	5
ISBN (Electronic)	9781509011841
DOIs	https://doi.org/10.1109/CSPA.2017.8064921
State	Published - 10 Oct 2017
Externally published	Yes

Publication series

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

Bibliographical note

Publisher Copyright:
© 2017 IEEE.

Keywords

Least mean square (LMS)
adaptive filter
complex LMS (CLMS)
complex signal
fractional LMS (FLMS)
fractional calculus
fractional complex LMS (FCLMS)
high convergence
low steady state error
plant identification

ASJC Scopus subject areas

Signal Processing
Instrumentation

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10.1109/CSPA.2017.8064921

Cite this

Ahmad, J., Khan, S., Usman, M., Naseem, I., Moinuddin, M., & Jamil Syed, H. (2017). FCLMS: Fractional complex LMS algorithm for complex system identification. In Proceedings - 2017 IEEE 13th International Colloquium on Signal Processing and its Applications, CSPA 2017 (pp. 39-43). Article 8064921 (Proceedings - 2017 IEEE 13th International Colloquium on Signal Processing and its Applications, CSPA 2017). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CSPA.2017.8064921

Ahmad, Jawwad ; Khan, Shujaat ; Usman, Muhammad et al. / FCLMS : Fractional complex LMS algorithm for complex system identification. Proceedings - 2017 IEEE 13th International Colloquium on Signal Processing and its Applications, CSPA 2017. Institute of Electrical and Electronics Engineers Inc., 2017. pp. 39-43 (Proceedings - 2017 IEEE 13th International Colloquium on Signal Processing and its Applications, CSPA 2017).

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title = "FCLMS: Fractional complex LMS algorithm for complex system identification",

abstract = "In this paper, a fractional order calculus based least mean square algorithm is proposed for complex system identification. The proposed algorithm, named as, fractional complex least mean square (FCLMS), successfully deals with the problem of complex error due to negative weights or complex input/output in the FLMS. For the evaluation purpose a complex linear system is considered. The FCLMS algorithm successfully identifies the complex system and achieve high convergence rate without compromising the steady state error. The convergence rate of the proposed FCLMS is two times better than that of the complex least mean square (CLMS).",

keywords = "Least mean square (LMS), adaptive filter, complex LMS (CLMS), complex signal, fractional LMS (FLMS), fractional calculus, fractional complex LMS (FCLMS), high convergence, low steady state error, plant identification",

author = "Jawwad Ahmad and Shujaat Khan and Muhammad Usman and Imran Naseem and Muhammad Moinuddin and \{Jamil Syed\}, Hassan",

note = "Publisher Copyright: {\textcopyright} 2017 IEEE.",

year = "2017",

month = oct,

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doi = "10.1109/CSPA.2017.8064921",

language = "English",

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

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Ahmad, J, Khan, S, Usman, M, Naseem, I, Moinuddin, M & Jamil Syed, H 2017, FCLMS: Fractional complex LMS algorithm for complex system identification. in Proceedings - 2017 IEEE 13th International Colloquium on Signal Processing and its Applications, CSPA 2017., 8064921, Proceedings - 2017 IEEE 13th International Colloquium on Signal Processing and its Applications, CSPA 2017, Institute of Electrical and Electronics Engineers Inc., pp. 39-43. https://doi.org/10.1109/CSPA.2017.8064921

FCLMS: Fractional complex LMS algorithm for complex system identification. / Ahmad, Jawwad; Khan, Shujaat; Usman, Muhammad et al.
Proceedings - 2017 IEEE 13th International Colloquium on Signal Processing and its Applications, CSPA 2017. Institute of Electrical and Electronics Engineers Inc., 2017. p. 39-43 8064921 (Proceedings - 2017 IEEE 13th International Colloquium on Signal Processing and its Applications, CSPA 2017).

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

TY - GEN

T1 - FCLMS

T2 - Fractional complex LMS algorithm for complex system identification

AU - Ahmad, Jawwad

AU - Khan, Shujaat

AU - Usman, Muhammad

AU - Naseem, Imran

AU - Moinuddin, Muhammad

AU - Jamil Syed, Hassan

PY - 2017/10/10

Y1 - 2017/10/10

N2 - In this paper, a fractional order calculus based least mean square algorithm is proposed for complex system identification. The proposed algorithm, named as, fractional complex least mean square (FCLMS), successfully deals with the problem of complex error due to negative weights or complex input/output in the FLMS. For the evaluation purpose a complex linear system is considered. The FCLMS algorithm successfully identifies the complex system and achieve high convergence rate without compromising the steady state error. The convergence rate of the proposed FCLMS is two times better than that of the complex least mean square (CLMS).

AB - In this paper, a fractional order calculus based least mean square algorithm is proposed for complex system identification. The proposed algorithm, named as, fractional complex least mean square (FCLMS), successfully deals with the problem of complex error due to negative weights or complex input/output in the FLMS. For the evaluation purpose a complex linear system is considered. The FCLMS algorithm successfully identifies the complex system and achieve high convergence rate without compromising the steady state error. The convergence rate of the proposed FCLMS is two times better than that of the complex least mean square (CLMS).

KW - Least mean square (LMS)

KW - adaptive filter

KW - complex LMS (CLMS)

KW - complex signal

KW - fractional LMS (FLMS)

KW - fractional calculus

KW - fractional complex LMS (FCLMS)

KW - high convergence

KW - low steady state error

KW - plant identification

UR - https://www.scopus.com/pages/publications/85028060969

U2 - 10.1109/CSPA.2017.8064921

DO - 10.1109/CSPA.2017.8064921

M3 - Conference contribution

AN - SCOPUS:85028060969

T3 - Proceedings - 2017 IEEE 13th International Colloquium on Signal Processing and its Applications, CSPA 2017

SP - 39

EP - 43

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

PB - Institute of Electrical and Electronics Engineers Inc.

ER -

Ahmad J, Khan S, Usman M, Naseem I, Moinuddin M, Jamil Syed H. FCLMS: Fractional complex LMS algorithm for complex system identification. In Proceedings - 2017 IEEE 13th International Colloquium on Signal Processing and its Applications, CSPA 2017. Institute of Electrical and Electronics Engineers Inc. 2017. p. 39-43. 8064921. (Proceedings - 2017 IEEE 13th International Colloquium on Signal Processing and its Applications, CSPA 2017). doi: 10.1109/CSPA.2017.8064921

FCLMS: Fractional complex LMS algorithm for complex system identification

Abstract

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Bibliographical note

Keywords

ASJC Scopus subject areas

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