Skip to main content
Log in

Design and Analysis of the Fractional-Order Complex Least Mean Square (FoCLMS) Algorithm

  • Published:
Circuits, Systems, and Signal Processing Aims and scope Submit manuscript

Abstract

In this work, a new class of stochastic gradient algorithm is developed based on fractional calculus. Unlike the existing algorithms, the concept of complex fractional gradient is introduced by employing Caputo’s fractional derivative which results in a fractional steepest descent algorithm and a fractional-order complex LMS (FoCLMS) algorithm. We demonstrate that with the Caputo’s fractional gradient definition, the Weiner solution remains invariant. Convergence analysis of the proposed FoCLMS algorithm is presented for both transient and steady state scenarios. Consequently, expressions for the learning curves and steady state EMSE are derived. Our theoretical developments are validated by simulation experiments. Extensive simulations are presented to investigate all possible scenarios: channel with negative weights and real input data, channel with positive weights and complex input data, and channel with complex weights and complex input data.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

Data Availability

There is no data associated with this manuscript.

References

  1. T. Aboulnasr, K. Mayyas, A robust variable step-size LMS-type algorithm: analysis and simulations. IEEE Trans. Signal Process. 45(3), 631–639 (1997)

    Article  Google Scholar 

  2. J. Ahmad, S. Khan, M. Usman, I. Naseem, M. Moinuddin, FCLMS: Fractional complex LMS algorithm for complex system identification. In: 13th IEEE Colloquium on Signal Processing and its Applications (CSPA 2017). IEEE (2017)

  3. J. Ahmad, M. Usman, S. Khan, I. Naseem, H.J. Syed, RVP-FLMS: a robust variable power fractional LMS algorithm. In: 2016 IEEE International Conference on Control System, Computing and Engineering (ICCSCE). IEEE (2016)

  4. A. Ahmed, M. Moinuddin, U.M. Al-Saggaf, q-state space least mean family of algorithms. Circuits Syst. Signal Process. 37(2), 729–751 (2018)

    Article  MathSciNet  Google Scholar 

  5. U.M. Al-Saggaf, M. Moinuddin, M. Arif, A. Zerguine, The q-least mean squares algorithm. Sig. Process. 111, 50–60 (2015)

    Article  Google Scholar 

  6. J. Benesty, S.L. Gay, An improved pnlms algorithm. In: 2002 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), vol. 2, pp. II–1881. IEEE (2002)

  7. N.J. Bershad, F. Wen, H.C. So, Comments on fractional LMS algorithm. Signal Process. 133, 219–226 (2017)

    Article  Google Scholar 

  8. G.W. Bohannan, Analog fractional order controller in temperature and motor control applications. J. Vib. Control 14(9–10), 1487–1498 (2008)

    Article  MathSciNet  Google Scholar 

  9. B. Chen, S. Zhao, P. Zhu, J.C. Principe, Quantized kernel least mean square algorithm. IEEE Trans. Neural Netw. Learn. Syst. 23(1), 22–32 (2012)

    Article  Google Scholar 

  10. S. Cheng, Y. Wei, Y. Chen, Y. Li, Y. Wang, An innovative fractional order LMS based on variable initial value and gradient order. Sig. Process. 133, 260–269 (2017)

    Article  Google Scholar 

  11. S. Cheng, Y. Wei, Y. Chen, L. Xiaojian, Y. Wang, A novel fractional order normalized LMS algorithm with direction optimization. IFAC-PapersOnLine 49(9), 180–185 (2016)

    Article  MathSciNet  Google Scholar 

  12. S. Ciochină, C. Paleologu, J. Benesty, An optimized NLMS algorithm for system identification. Sig. Process. 118, 115–121 (2016)

    Article  Google Scholar 

  13. S.C. Douglas, A family of normalized LMS algorithms. IEEE Signal Process. Lett. 1(3), 49–51 (1994)

    Article  Google Scholar 

  14. S.K. Dubey, N.K. Rout, FLMS algorithm for acoustic echo cancellation and its comparison with lms. In: 2012 1st International Conference on Recent Advances in Information Technology (RAIT), pp. 852–856. IEEE (2012)

  15. J.M. Górriz, J. Ramírez, S. Cruces-Alvarez, C.G. Puntonet, E.W. Lang, D. Erdogmus, A novel LMS algorithm applied to adaptive noise cancellation. IEEE Signal Process. Lett. 16(1), 34–37 (2009)

    Article  Google Scholar 

  16. R.E. Gutiérrez, J.M. Rosário, J. Tenreiro Machado, Fractional order calculus: basic concepts and engineering applications. Math. Probl. Eng. 2010 (2010)

  17. S.S. Haykin, Adaptive Filter Theory. Pearson Education India (2008)

  18. R.W. Harris, D.M. Chabries, A variable stepsize (vs) algorithm. IEEE Trans. Acoustic Speech Signal Process. 34, 499–510 (1986)

    Article  Google Scholar 

  19. R. Hunger, An introduction to complex differentials and complex differentiability (2007)

  20. S. Javidi, M. Pedzisz, S.L. Goh, D.P. Mandic, The augmented complex least mean square algorithm with application to adaptive prediction problems 1. In: 2008 1st IAPR Workshop on Cognitive Information Processing. EURASIP (2008)

  21. A. Khalili, A. Rastegarnia, W.M. Bazzi, Z. Yang, Derivation and analysis of incremental augmented complex least mean square algorithm. IET Signal Process. 9(4), 312–319 (2015)

    Article  Google Scholar 

  22. A. Khalili, A. Rastegarnia, S. Sanei, Quantized augmented complex least-mean square algorithm: derivation and performance analysis. Sig. Process. 121, 54–59 (2016)

    Article  Google Scholar 

  23. S. Khan, J. Ahmad, I. Naseem, M. Moinuddin, A novel fractional gradient-based learning algorithm for recurrent neural networks. Circuits Syst. Signal Process. 37(2), 593–612 (2018)

    Article  MathSciNet  Google Scholar 

  24. S. Khan, I. Naseem, M.A. Malik, R. Togneri, M. Bennamoun, A fractional gradient descent-based RBF neural network. Circuits Syst. Signal Process. 1–22 (2018)

  25. S. Khan, I. Naseem, A. Sadiq, J. Ahmad, M. Moinuddin, Comments on “momentum fractional LMS for power signal parameter estimation”. arXiv:1805.07640 (2018)

  26. S. Khan, M. Usman, I. Naseem, R. Togneri, M. Bennamoun, A robust variable step size fractional least mean square (RVSS-FLMS) algorithm. In: 13th IEEE Colloquium on Signal Processing and its Applications (CSPA 2017). IEEE (2017)

  27. S. Khan, M. Usman, I. Naseem, R. Togneri, M. Bennamoun, VP-FLMS: a novel variable power fractional LMS algorithm. In: 2017 9th International Conference on Ubiquitous and Future Networks (ICUFN) (ICUFN 2017). Milan, Italy (2017)

  28. S. Khan, A. Wahab, I. Naseem, M. Moinuddin, Comments on design of fractional-order variants of complex LMS and NLMS algorithms for adaptive channel equalization. Nonlinear Dyn. 101(2), 1053–1060 (2020)

    Article  Google Scholar 

  29. B. Krishna, K. Reddy, Active and passive realization of fractance device of order 1/2. Act. Passive Electron. Compon. 2008, 1–5 (2008)

    Article  Google Scholar 

  30. M.F. Lima, J.A.T. Machado, M.M. Crisóstomo, Experimental signal analysis of robot impacts in a fractional calculus perspective. JACIII 11(9), 1079–1085 (2007)

    Article  Google Scholar 

  31. W. Liu, P.P. Pokharel, J.C. Principe, The kernel least-mean-square algorithm. IEEE Trans. Signal Process. 56(2), 543–554 (2008)

    Article  MathSciNet  Google Scholar 

  32. A. Loverro, Fractional Calculus: History, Definitions and Applications for the Engineer, Rapport Technique, Department of Aerospace and Mechanical Engineering, Univeristy of Notre Dame (2004)

  33. J. Lovoie, T.J. Osler, R. Tremblay, Fractional derivatives and special functions. SIAM Rev. 18(2), 240–268 (1976)

    Article  MathSciNet  Google Scholar 

  34. R. Magin, M. Ovadia, Modeling the cardiac tissue electrode interface using fractional calculus. J. Vib. Control 14(9–10), 1431–1442 (2008)

    Article  Google Scholar 

  35. B. Mathieu, P. Melchior, A. Oustaloup, C. Ceyral, Fractional differentiation for edge detection. Sig. Process. 83(11), 2421–2432 (2003)

    Article  Google Scholar 

  36. S.G. Osgouei, M. Geravanchizadeh, Speech enhancement using convex combination of fractional least-mean-squares algorithm. In: 2010 5th International Symposium on Telecommunications (IST), pp. 869–872. IEEE (2010)

  37. R. Panda, M. Dash, Fractional generalized splines and signal processing. Sig. Process. 86(9), 2340–2350 (2006)

    Article  Google Scholar 

  38. Y. Pu, X. Yuan, K. Liao, J. Zhou, N. Zhang, X. Pu, Y. Zeng, A recursive two-circuits series analog fractance circuit for any order fractional calculus. In: ICO20: Optical Information Processing, pp. 60271Y–60271Y. International Society for Optics and Photonics (2006)

  39. Y. Pu, J. Zhou, Y. Zhang, N. Zhang, G. Huang, P. Siarry, Fractional extreme value adaptive training method: fractional steepest descent approach. IEEE Trans. Neural Netw. Learn. Syst. 26(4), 653–662 (2015). https://doi.org/10.1109/TNNLS.2013.2286175

    Article  MathSciNet  Google Scholar 

  40. J. Rosario, D. Dumur, J.T. Machado, Analysis of fractional-order robot axis dynamics. Fract. Differ. Appl. 2, 367–372 (2006)

    Google Scholar 

  41. A. Sadiq, S. Khan, I. Naseem, R. Togneri, M. Bennamoun, Enhanced q-least mean square. Circuits Syst. Signal Process. 38(10), 4817–4839 (2019)

    Article  Google Scholar 

  42. A.H. Sayed, Adaptive Filters (Wiley, 2008)

  43. S.M. Shah, R. Samar, N.M. Khan, M.A.Z. Raja, Design of fractional-order variants of complex LMS and NLMS algorithms for adaptive channel equalization. Nonlinear Dyn. 1–20 (2016)

  44. L. Sommacal, P. Melchior, A. Oustaloup, J.M. Cabelguen, A.J. Ijspeert, Fractional multi-models of the frog gastrocnemius muscle. J. Vib. Control 14(9–10), 1415–1430 (2008)

    Article  Google Scholar 

  45. J.I. Suárez, B.M. Vinagre, A. Calderón, C. Monje, Y. Chen, Using fractional calculus for lateral and longitudinal control of autonomous vehicles. In: International Conference on Computer Aided Systems Theory, pp. 337–348. Springer (2003)

  46. Y. Tan, Z. He, B. Tian, A novel generalization of modified LMS algorithm to fractional order. IEEE Signal Process. Lett. 22(9), 1244–1248 (2015)

    Article  Google Scholar 

  47. N.V. Thakor, Y.S. Zhu, Applications of adaptive filtering to ECG analysis: noise cancellation and arrhythmia detection. IEEE Trans. Biomed. Eng. 38(8), 785–794 (1991)

    Article  Google Scholar 

  48. F.A. Tobar, A. Kuh, D.P. Mandic, A novel augmented complex valued kernel LMS. In: 2012 IEEE 7th Sensor Array and Multichannel Signal Processing Workshop (SAM), pp. 473–476. IEEE (2012)

  49. A. Wahab, S. Khan, Comments on fractional extreme value adaptive training method: fractional steepest descent approach. IEEE Trans. Neural Netw. Learn. Syst. 31(3), 1066–1068 (2019)

    Article  MathSciNet  Google Scholar 

  50. A. Wahab, S. Khan, Comments on “generalization of the gradient method with fractional order gradient direction”. arXiv:2009.05221 (2020)

  51. A. Wahab, S. Khan, F.Z. Khan, Comments on “a new computing approach for power signal modeling using fractional adaptive algorithms”. arXiv:2003.09597 (2020)

  52. A. Wahab, S. Khan, F.Z. Khan, Comments on “design of momentum fractional lms for hammerstein nonlinear system identification with application to electrically stimulated muscle model”. arXiv:2009.07076 (2020)

  53. M. Weilbeer, Efficient numerical methods for fractional differential equations and their analytical background. Papierflieger (2005)

  54. B. Widrow, J. McCool, M. Ball, The complex LMS algorithm. In: IEEE Proceedings, vol. 63, p. 719 (1975)

  55. R.M.A. Zahoor, I.M. Qureshi, A modified least mean square algorithm using fractional derivative and its application to system identification. Eur. J. Sci. Res. 35(1), 14–21 (2009)

    Google Scholar 

  56. S. Zhou, H. Zhao, W. Wang, A fraction normalized subband adaptive filter algorithm. In: 2016 35th Chinese Control Conference (CCC), pp. 3095–3098. TCCT (2016)

  57. Y. Zhu-Zhong, Z. Ji-Liu, An improved design for the IIR-type digital fractional order differential filter. In: International Seminar on Future BioMedical Information Engineering, 2008. FBIE’08. pp. 473–476. IEEE (2008)

Download references

Funding

There is no funding for this work.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Jawwad Ahmad.

Ethics declarations

Conflict of interest

Authors do not have any conflict of interest for the submitted work.

Code Availability

Code can be made available on request.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ahmad, J., Zubair, M., Rizvi, S.S.H. et al. Design and Analysis of the Fractional-Order Complex Least Mean Square (FoCLMS) Algorithm. Circuits Syst Signal Process 40, 5152–5181 (2021). https://doi.org/10.1007/s00034-021-01715-8

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00034-021-01715-8

Keywords

Navigation