Nothing Special   »   [go: up one dir, main page]
Skip to main content
Springer Nature Link
Log in

A new design method for adaptive IIR system identification using hybrid CPSO and DE

  • Original Paper
  • Published:
Nonlinear Dynamics Aims and scope Submit manuscript

Abstract

Adaptive infinite impulse response filters have received much attention due to its utilization in a wide range of real-world applications. The design of the IIR filters poses a typically nonlinear, non-differentiable and multimodal problem in the estimation of the coefficient parameters. The aim of the current study is the application of a novel hybrid optimization technique based on the combination of cellular particle swarm optimization and differential evolution called CPSO–DE for the optimal parameter estimation of IIR filters. DE is used as the evolution rule of the cellular part in CPSO to improve the performance of the original CPSO. Benchmark IIR systems commonly used in the specialized literature have been selected for tuning the parameters and demonstrating the effectiveness of the CPSO–DE method. The proposed CPSO–DE method is experimentally compared with two new design methods: the tissue-like membrane system (TMS), the hybrid particle swarm optimization and gravitational search algorithm (HPSO–GSA), the original CPSO-outer and CPSO-inner, and classical implementations of PSO, GSA and DE. Computational results and comparison of CPSO–DE with the other evolutionary and hybrid methods show satisfactory results. The hybridization of CPSO and DE demonstrates powerful estimation ability. In particular, to our knowledge, this hybridization has not yet been investigated for the IIR system identification.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

References

  1. Agrawal, N., Kumar, A., Bajaj, V.: Hybrid method based optimized design of digital iir filter. In: Communications and Signal Processing (ICCSP), International Conference on 2015, pp. 1549–1554. IEEE (2015)

  2. Chen, S., Luk, B.L.: Digital iir filter design using particle swarm optimisation. Int. J. Modell. Identif. Control 9(4), 327–335 (2010)

    Article  Google Scholar 

  3. Cuevas, E., Gálvez, J., Hinojosa, S., Avalos, O., Zaldívar, D., Pérez-Cisneros, M.: A comparison of evolutionary computation techniques for iir model identification. J. Appl. Math. 2014 (2014)

  4. Diniz, P.S.: Adaptive Filtering: Algorithms ans Practical Implementation. Springer, Berlin (2013)

    Book  MATH  Google Scholar 

  5. Eberhart, R.C., Shi, Y.: Particle swarm optimization: developments, applications and resources. In: Evolutionary Computation, 2001. Proceedings of the 2001 Congress on, vol. 1, pp. 81–86. IEEE (2001)

  6. Gao, L., Huang, J., Li, X.: An effective cellular particle swarm optimization for parameters optimization of a multi-pass milling process. Appl. Soft Comput. 12(11), 3490–3499 (2012)

    Article  Google Scholar 

  7. Gao, L., Li, X., Wen, X., Lu, C., Wen, F.: A hybrid algorithm based on a new neighborhood structure evaluation method for job shop scheduling problem. Comput. Ind. Eng. 88, 417–429 (2015)

    Article  Google Scholar 

  8. Gao, Y., Li, Y., Qian, H.: The design of iir digital filter based on chaos particle swarm optimization algorithm. In: Genetic and Evolutionary Computing, 2008. WGEC’08. Second International Conference on, pp. 303–306. IEEE (2008)

  9. Gao, Z., Liao, X.: Rational approximation for fractional-order system by particle swarm optimization. Nonlinear Dyn. 67(2), 1387–1395 (2012)

    Article  MathSciNet  MATH  Google Scholar 

  10. Gholizadeh, S.: Layout optimization of truss structures by hybridizing cellular automata and particle swarm optimization. Comput. Struct. 125, 86–99 (2013)

    Article  Google Scholar 

  11. Hou, Z., LU, Z.S.: Particle swarm optimization algorithm for iir digital filters design. J. Circuits Syst. 8(4), 16–20 (2003)

    Google Scholar 

  12. Jiang, S., Wang, Y., Ji, Z.: A new design method for adaptive iir system identification using hybrid particle swarm optimization and gravitational search algorithm. Nonlinear Dyn. 79(4), 2553–2576 (2015)

    Article  MathSciNet  Google Scholar 

  13. Karaboga, N., Cetinkaya, B.: Design of minimum phase digital iir filters by using genetic algorithm. In: Proceedings of the 6th Nordic signal Processing Symposium-NORSIG, vol. 2004 (2004)

  14. Karaboga, N., Kalinli, A., Karaboga, D.: Designing digital iir filters using ant colony optimisation algorithm. Eng. Appl. Artif. Intell. 17(3), 301–309 (2004)

    Article  MATH  Google Scholar 

  15. Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Neural Networks, 1995. Proceedings IEEE International Conference on, vol. 4, pp. 1942–1948. IEEE (1995)

  16. Kennedy, J., Kennedy, J.F., Eberhart, R.C., Shi, Y.: Swarm intelligence. Morgan Kaufmann (2001)

  17. Krusienski, D., Jenkins, W.: Adaptive filtering via particle swarm optimization. In: Signals, Systems and Computers, 2004. Conference Record of the Thirty-Seventh Asilomar Conference on, vol. 1, pp. 571–575. IEEE (2003)

  18. Krusienski, D., Jenkins, W.: Design and performance of adaptive systems based on structured stochastic optimization strategies. Circuits Syst. Mag. IEEE 5(1), 8–20 (2005)

    Article  Google Scholar 

  19. Krusienski, D.J., Jenkins, W.K.: Particle swarm optimization for adaptive iir filter structures. In: Evolutionary Computation, 2004. CEC2004. Congress on, vol. 1, pp. 965–970. IEEE (2004)

  20. Luitel, B., Venayagamoorthy, G.K.: Differential evolution particle swarm optimization for digital filter design. In: Evolutionary Computation, 2008. CEC 2008.(IEEE World Congress on Computational Intelligence). IEEE Congress on, pp. 3954–3961. IEEE (2008)

  21. Ma, Q., Cowan, C.F.: Genetic algorithms applied to the adaptation of iir filters. Signal Process. 48(2), 155–163 (1996)

    Article  MATH  Google Scholar 

  22. McIntosh, H.V.: One Dimensional Cellular Automata. Luniver Press, Bristol (2009)

    Google Scholar 

  23. Mostajabi, T., Poshtan, J., Mostajabi, Z.: Iir model identification via evolutionary algorithms. Artif. Intell. Rev. 44(1), 87–101 (2015)

    Article  Google Scholar 

  24. Nayeri, M., Jenkins, W.: Alternate realizations to adaptive iir filters and properties of their performance surfaces. IEEE Trans. Circuits Syst. 36(4), 485–496 (1989)

    Article  MathSciNet  Google Scholar 

  25. Netto, S.L., Diniz, P.S., Agathoklis, P.: Adaptive iir filtering algorithms for system identification: a general framework. Edu. IEEE Trans. 38(1), 54–66 (1995)

    Article  Google Scholar 

  26. Ng, S., Leung, S., Chung, C., Luk, A., Lau, W.: The genetic search approach. A new learning algorithm for adaptive iir filtering. Signal Process. Mag. IEEE 13(6), 38–46 (1996)

    Article  Google Scholar 

  27. Panda, G., Pradhan, P.M., Majhi, B.: Iir system identification using cat swarm optimization. Expert Syst. Appl. 38(10), 12671–12683 (2011)

    Article  Google Scholar 

  28. Peng, H., Wang, J.: A hybrid approach based on tissue p systems and artificial bee colony for iir system identification. Neural Computing and Applications, pp. 1–11 (2016)

  29. Price, K., Storn, R.: Differential evolution: a simple evolution strategy for fast optimization. Dr. Dobbs J. 22(4), 18–24 (1997)

    MATH  Google Scholar 

  30. Rashedi, E., Nezamabadi-Pour, H., Saryazdi, S.: Gsa: a gravitational search algorithm. Inf. Sci. 179(13), 2232–2248 (2009)

    Article  MATH  Google Scholar 

  31. Saha, S.K., Kar, R., Mandal, D., Ghoshal, S.: Optimal stable iir low pass filter design using modified firefly algorithm. In: Swarm, Evolutionary, and Memetic Computing, pp. 98–109. Springer (2013)

  32. Saha, S.K., Kar, R., Mandal, D., Ghoshal, S.P., Mukherjee, V.: A new design method using opposition-based bat algorithm for iir system identification problem. Int. J. Bio Inspir. Comput. 5(2), 99–132 (2013)

    Article  Google Scholar 

  33. Shafaati, M., Mojallali, H.: Modified firefly optimization for iir system identification. J. Control Eng. Appl. Inform. 14(4), 59–69 (2012)

    Google Scholar 

  34. Shi, Y., Eberhart, R.: A modified particle swarm optimizer. In: Evolutionary Computation Proceedings, 1998. IEEE World Congress on Computational Intelligence., The 1998 IEEE International Conference on, pp. 69–73. IEEE (1998)

  35. Shi, Y., Liu, H., Gao, L., Zhang, G.: Cellular particle swarm optimization. Inf. Sci. 181(20), 4460–4493 (2011)

    Article  MathSciNet  MATH  Google Scholar 

  36. Shynk, J.J.: Adaptive iir filtering. ASSP Mag. IEEE 6(2), 4–21 (1989)

    Article  Google Scholar 

  37. Singh, R., Verma, H.: Teaching–learning-based optimization algorithm for parameter identification in the design of iir filters. J. Inst. Eng. India Ser. B 94(4), 285–294 (2013)

    Article  Google Scholar 

  38. Storn, R.: On the usage of differential evolution for function optimization. In: Fuzzy Information Processing Society, 1996. NAFIPS., 1996 Biennial Conference of the North American, pp. 519–523. IEEE (1996)

  39. Storn, R., Price, K.: Differential evolution-a simple and efficient adaptive scheme for global optimization over continuous spaces, vol. 3. ICSI Berkeley (1995)

  40. Storn, R., Price, K.: Differential evolution-a simple and efficient heuristic for global optimization over continuous spaces. J. Glob. Optim. 11(4), 341–359 (1997)

    Article  MathSciNet  MATH  Google Scholar 

  41. Storn, R., Price, K.V.: Minimizing the real functions of the icec’96 contest by differential evolution. In: International Conference on Evolutionary Computation, pp. 842–844 (1996)

  42. Tang, K.S., Man, K.F., Kwong, S., He, Q.: Genetic algorithms and their applications. Signal Process. Mag. IEEE 13(6), 22–37 (1996)

    Article  Google Scholar 

  43. Tang, K.S., Man, K.F., Kwong, S., Liu, Z.F.: Design and optimization of iir filter structure using hierarchical genetic algorithms. Ind. Electron. IEEE Trans. 45(3), 481–487 (1998)

  44. Wang, J., Shi, P., Peng, H.: Membrane computing model for iir filter design. Inf. Sci. 329, 164–176 (2016)

    Article  Google Scholar 

Download references

Acknowledgements

This work was partially supported by National Council for Science and Technology (CONACYT) with project number CB-2014-237323.

Author information

Authors and Affiliations

  1. Area Academica de Ingenieria, Instituto de Ciencias Basicas e Ingenieria, Universidad Autonoma del Estado de Hidalgo, Carr. Pachuca-Tulancingo Km 4.5, 42184, Mineral de la Reforma, Hgo, Mexico

    Pedro Lagos-Eulogio, Juan Carlos Seck-Tuoh-Mora, Norberto Hernandez-Romero & Joselito Medina-Marin

Authors
  1. Pedro Lagos-Eulogio
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Juan Carlos Seck-Tuoh-Mora
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Norberto Hernandez-Romero
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Joselito Medina-Marin
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Juan Carlos Seck-Tuoh-Mora.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Lagos-Eulogio, P., Seck-Tuoh-Mora, J.C., Hernandez-Romero, N. et al. A new design method for adaptive IIR system identification using hybrid CPSO and DE. Nonlinear Dyn 88, 2371–2389 (2017). https://doi.org/10.1007/s11071-017-3383-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11071-017-3383-7

Keywords

Search

Navigation