Skip to main content

Reduced Complexity Dynamic Systems Using Approximate Control Moments

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

    We’re sorry, something doesn't seem to be working properly.

    Please try refreshing the page. If that doesn't work, please contact support so we can address the problem.

Abstract

This paper deals with reduction of computational complexities in dynamic systems. This paper develops a novel method of reducing complexities with use of control moments of the system. Though the proposed method is validated through channel estimation in this paper, the same can be equally applied to any other dynamic systems. Encouraging results given in this paper prove that the computational complexities can be reduced up to 104 with a marginal affordable loss of performance.

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

Access this article

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.

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

References

  1. M. Bardet, I. Boussaada, Complexity reduction of C-algorithm. Appl. Math. Comput. 217(17), 7318–7323 (2011)

    Article  MathSciNet  MATH  Google Scholar 

  2. C. Beattie, S. Gugercin, S. Wyatt, Inexact solves in interpolatory model reduction. Linear Algebra Appl. 436(8), 2916–2943 (2012)

    Article  MATH  Google Scholar 

  3. A. Bentayeb, N. Maamri, D. Mehdi, Moments based synthesis approach: comparison with H design, in IFAC DECOM-TT, Istanbul, Turkey (2003)

    Google Scholar 

  4. V.A. Chandrasetty, S.M. Aziz, An area efficient LDPC decoder using a reduced complexity min-sum algorithm. Integr. VLSI J. 45(2), 141–148 (2012)

    Article  Google Scholar 

  5. Z. Ding, L. Ye, Blind Equalization and Identification (Dekker, New York, 2001)

    Google Scholar 

  6. T. Gueddar, V. Dua, Disaggregation–aggregation based model reduction for refinery-wide optimization. Comput. Chem. Eng. 35(9), 1838–1856 (2011)

    Article  Google Scholar 

  7. H.-L. Hung, Using evolutionary computation technique for trade-off between performance peak-to average power ration reduction and computational complexity in OFDM systems. Comput. Electr. Eng. 37(1), 57–70 (2011)

    Article  Google Scholar 

  8. M. Krasnyk, M. Mangold, S. Ganesan, L. Tobiska, Numerical reduction of a crystallizer model with internal and external coordinates by proper orthogonal decomposition. Chem. Eng. Sci. 70, 77–86 (2012)

    Article  Google Scholar 

  9. E. Kreyzig, Advanced Engineering Mathematics, 8th edn. (Wiley, New York, 1999)

    Google Scholar 

  10. E.V. Kurmyshev, J.T. Guillen-Bonilla, Complexity reduced coding of binary pattern units in image classification. Opt. Lasers Eng. 49(6), 718–722 (2011)

    Article  Google Scholar 

  11. S. Li, L.-M. Song, T.-S. Qiu, Steady-state and tracking analysis of fractional lower-order constant modulus algorithm. Circuits Syst. Signal Process. 30(6), 1275–1288 (2011)

    Article  MATH  Google Scholar 

  12. X. Li, J. Dezert, F. Smarandache, X. Huang, Evidence supporting measure of similarity for reducing the complexity in information fusion. Inf. Sci. 181(10), 1818–1835 (2011)

    Article  MathSciNet  Google Scholar 

  13. Y.-T. Li, Z. Bai, W.-W. Lin, Y. Su, A structured quasi-Arnoldi procedure for model order reduction of second-order systems. Linear Algebra Appl. 436(8), 2780–2794 (2012)

    Article  MATH  Google Scholar 

  14. T.-T. Lin, F.-H. Hwang, A novel CFO estimator with joint bisection-searching and complexity reduction technique for uplink MC-CDMA systems. Expert Syst. Appl. 39(3), 3145–3152 (2012)

    Article  Google Scholar 

  15. R. Lu, H. Li, A. Xue, J. Zheng, Q. She, Quantized H filtering for different communication channels. Circuits Syst. Signal Process. 31(2), 501–519 (2012)

    Article  MathSciNet  MATH  Google Scholar 

  16. N. Maamri, J.C. Trigeassou, PID design for time delayed systems by the method of moments, in European Control Conference, Groningen, Holland (1993)

    Google Scholar 

  17. N. Maamri, A. Bentayeb, J.-C. Trigeassou, Design and iterative optimization of reduced robust controllers with equality-constraints, in ROCOND, Milan (2003)

    Google Scholar 

  18. D.W. Marquardt, An algorithm for least-squares estimation of non linear parameters. J. Soc. Ind. Appl. Math. 11(2), 431–441 (1963)

    Article  MathSciNet  MATH  Google Scholar 

  19. F. Monroux, Méthodologie générale de synthèse de correcteurs par la méthode des moments, approche mixte: fréquentielle et temporelle, Thèse de doctorat, Université de Poitiers (1999)

  20. S.L. Napelenok, K.M. Foley, D. Kang, R. Mathur, T. Pierce, S. Trivikrama Rao, Dynamic evaluation of regional air quality model’s response to emission reduction in the presence of uncertain emission inventories. Atmos. Environ. 45(24), 4091–4098 (2011)

    Article  Google Scholar 

  21. R.O. Preda, D.N. Vizireanu, A robust wavelet based video watermarking scheme for copyright protection using the human visual system. J. Electron. Imaging 20, 013022 (2011)

    Article  Google Scholar 

  22. L. Scrucca, Model-based SIR for dimension reduction. Comput. Stat. Data Anal. 55(11), 3010–3026 (2011)

    Article  MathSciNet  MATH  Google Scholar 

  23. A.M. Tartakovsky, A. Panchenko, K. Ferris, Dimension reduction method for ODE fluid models. J. Comput. Phys. 230(23), 8554–8572 (2011)

    Article  MathSciNet  MATH  Google Scholar 

  24. D.N. Vizireanu, S. Halunga, G. Marghescu, Morphological skeleton decomposition interframe interpolation method. J. Electron. Imaging 19(2), 023018 (2010)

    Article  Google Scholar 

  25. D.N. Vizireanu, Morphological shape decomposition interframe interpolation method. J. Electron. Imaging 17, 013007 (2008)

    Article  Google Scholar 

  26. D.N. Vizireanu Generalizations of binary morphological shape decomposition. J. Electron. Imaging 16(1), 01302 (2007)

    Article  Google Scholar 

  27. A.C. Yang, S.-J. Tsai, C.-H. Yang, C.-H. Kuo, T.-J. Chen, C.-J. Hong, Reduced physiologic complexity is associated with poor sleep in patients with major depression and primary insomnia. J. Affect. Disord. 131(1–3), 179–185 (2011)

    Google Scholar 

  28. H. Yoo, F. Guilloud, R. Pyndiah, PAPR reduction for LDPC coded OFDM systems using binary masks and optimal LLR estimation. Signal Process. 91(11), 2606–2614 (2011)

    Article  Google Scholar 

  29. H. Zhang, L. Li, W. Li, Independent component analysis based on fast proximal gradient. Circuits Syst. Signal Process. 31(2), 583–593 (2012)

    Article  MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Siba Prasada Panigrahi.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Panda, R.N., Padhy, S.K., Prasad, S. et al. Reduced Complexity Dynamic Systems Using Approximate Control Moments. Circuits Syst Signal Process 31, 1731–1744 (2012). https://doi.org/10.1007/s00034-012-9406-1

Download citation

  • Received:

  • Revised:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00034-012-9406-1

Keywords