Call for Paper - November 2020 Edition
IJCA solicits original research papers for the November 2020 Edition. Last date of manuscript submission is October 20, 2020. Read More

Adaptive IIR and FIR Filtering using Evolutionary LMS Algorithm in View of System Identification

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2018
Ibraheem Kasim Ibraheem

Ibraheem Kasim Ibraheem. Adaptive IIR and FIR Filtering using Evolutionary LMS Algorithm in View of System Identification. International Journal of Computer Applications 182(11):31-39, August 2018. BibTeX

	author = {Ibraheem Kasim Ibraheem},
	title = {Adaptive IIR and FIR Filtering using Evolutionary LMS Algorithm in View of System Identification},
	journal = {International Journal of Computer Applications},
	issue_date = {August 2018},
	volume = {182},
	number = {11},
	month = {Aug},
	year = {2018},
	issn = {0975-8887},
	pages = {31-39},
	numpages = {9},
	url = {},
	doi = {10.5120/ijca2018917740},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}


Our aim in this paper is to show how simple adaptive IIR filter can be used in system identification. The main objective of our research is to study the LMS algorithm and its improvement by the genetic search approach, namely, LMS-GA, to search the multi-modal error surface of the adaptive IIR filter to avoid local minima and finding the optimal weight vector when only measured or estimated data are available. Convergence analysis of the LMS algorithm in the case of colored input signal, i.e., correlated input signal is demonstrated via the input’s power spectral density and the Fourier transform of the autocorrelation matrix of the input signal. Simulations have been carried out on adaptive filtering of IIR filter and tested on white and colored input signals to validate the powerfulness of the genetic-based LMS algorithm.


  1. S. D. S. Bernard Widrow, 1985. Adaptive Signal Processing. Prentice-Hall.
  2. J. C. S. and J. V. J. Gerardo Avalos. 2011. Applications of Adaptive Filtering. in Adaptive Filtering Applications, Lino Garcia Morales, Ed. InTech, pp. 1–20.
  3. M. Shams Esfand Abadi, H. Mesgarani, and S. M. Khademiyan. 2017. The wavelet transform-domain LMS adaptive filter employing dynamic selection of subband-coefficients. Digit. Signal Process., vol. 69, pp. 94–105, 2017.
  4. C. Y. C. S.C. Ng, S.H. Leung. 1996. The Genetic Search Approach: A new Learning Algorithm for Adaptive IIR Filtering. IEEE Signal Process. Mag., vol. 13, no. 6, pp. 38–46.
  5. S. M. Kumpati S. Narendra. 1997. Neural Networks for System Identification. IFAC System Identification, Vol. 30, No. 11, pp. 735–742.
  6. D. R. Santosh Kumar Behera. 2014. System Identification Using Recurrent Neural Network. Int. J. Adv. Res. Electr. Electron. Instrum. Eng., Vol. 3, No. 3, pp. 8111–8117.
  7. H. Jaeger. 2003. Adaptive nonlinear system identification with echo state networks. Advances in Neural Information Processing Systems, pp. 593–600.
  8. W. Zhang. 2007. System Identification Based on a Generalized ADALINE Neural Network. American Control Conference (ACC), 2007, Vol. 11, No. 1, pp. 4792–4797.
  9. Ibraheem. Kasim Ibraheem. 2017. System Identification of Thermal Process using Elman Neural Networks with No Prior Knowledge of System Dynamics. Int. J. Comput. Appl., Vol. 161, No. 11, pp. 38–46.
  10. V. Katari, S. Malireddi, S. K. S. Bendapudi, and G. Panda. 2008. Adaptive nonlinear system identification using comprehensive learning PSO. 3rd International Symposium on Communications, Control and Signal Processing( ISCCSP), pp. 434–439.
  11. A. C. Sinha, Rashmi. 2017. Adaptive Filtering Via Wind Driven Optimization Technique. 3rd IEEE International Conference on Computational Intelligence and Communication Technolog (CICT), pp. 1–5.
  12. Q. L. Qian Zhang, Sa Wu. 2015. A PSO identification algorithm for temperature adaptive adjustment system. IEEE International Conference on Industrial Engineering and Engineering Management (IEEM), Singapore, Singapore, pp. 752–755.
  13. J. Zhang and P. Xia. 2017. An improved PSO algorithm for parameter identification of nonlinear dynamic hysteretic models,” J. Sound Vib., vol. 389, pp. 153–167.
  14. A. Sarangi, S. K. Sarangi, M. Mukherjee, and S. P. Panigrahi. 2015. System identification by Crazy-cat swarm optimization. 2015 International Conference on Microwave, Optical and Communication Engineering (ICMOCE), pp. 439–442.
  15. K. K. A.-M. Thamer M. Jamel. 2012. Simple Variable Step Size LMS Algorithm for Adaptive Identification of IIR Filtering System. 5th International Conference on Communications, Computers and Applications (MIC-CCA), Istanbul, Turkey, pp. 23–28.
  16. S. A. Ghauri and M. F. Sohail. 2013. System identification using LMS, NLMS and RLS. Proceeding - IEEE Student Conference on Research and Development, (SCOReD), no. December, pp. 65–69.
  17. L. Lu and H. Zhao. 2015. A novel convex combination of LMS adaptive filter for system identification. 12th International Conference on Signal Processing Proceedings (ICSP), Hangzhou, China pp. 225–229.
  18. R. Yu, Y. Song, and M. Nambiar. 2014. Fast system identification using prominent subspace LMS. Digit. Signal Process. A Rev. J., Vol. 27, No. 1, pp. 44–56,.
  19. F. Titel and K. Belarbi. 2013. Identification of Dynamic systems using a Genetic Algorithm-based Fuzzy Wavelet Neural Network approach,” in Proceedings of the 3rd International Conference on Systems and Control, 2013, pp. 6–11.
  20. Alyaa. A. AL-Husainy, Ibraheem Kasim Ibraheem. 2014. Application of an Evolutionary Optimization Technique to Routing in Mobile Wireless Networks Application of an Evolutionary Optimization Technique to Routing in Mobile Wireless Network. Int. J. Comput. Appl., Vol. 99, No. 7, pp. 24–31,.
  21. Ibraheem. K. Ibraheem and Alyaa. A. AL-Husainy. 2015. Design of a Double-objective QoS Routing in Dynamic Wireless Networks using Evolutionary Adaptive Genetic Algorithm. Int. J. Adv. Res. Comput. Commun. Eng., Vol. 4, No. 9, pp. 156–165.


IIR filter, LMS algorithm, genetic algorithm, colored signals, power spectral density, multi-modal error surface, autocorrelation matrix.