IDENTIFICATION OF MULTI-DIMENSIONAL SYSTEM BASED ON A NOVEL CRITERION

Yue Zhao, Kueiming Lo, Wook-Hyun Kwon

Abstract

Most system recursive identification algorithms are based on the prediction error (PE) criterion. Such a recursive algorithm only considers the present estimation residual error instead of all estimation residuals. It would result in large estimation error when the signal noise disturbs strongly. In this paper, a new identification criterion is proposed. It considers both the errors between the actual outputs and the estimation result and the difference of each estimation error. Under this criterion, a new recursive algorithm MSDCN (Multi-dimensional System Disturbed by Color Noise) is proposed. For multi-dimensional systems, weighting different values on the estimation errors and the difference of each error, MSDCN could both decrease the estimation errors and got smooth prediction curves. Several simulation examples are given to illustrate the method’s anti-disturbance performance.

References

  1. Andersson, A. and Broman, H. (1998), A second-order recursive algorithm with applications to adaptive filtering and subspace tracking. IEEE Trans. Signal Processing, vol. 46, pp. 1720C1725, June.
  2. Griffith Jr, D. W. and Arce, G. R. (1999), A partially decoupled RLS algorithm for volterra filters. IEEE Trans. Signal Processing, vol. 47, pp. 579C582, Feb.
  3. Kuo, C. J. , Jr, R. D. , Lin, C. Y. and Tsai, Y. C. (2000), Settheoretic estimation based on a priori knowledge of the noise distribution. IEEE Trans. Signal Processing, vol. 48, pp. 2150C2156, July.
  4. Lo, K. M. , et al. (2006), Empirical frequency-domain optimal parameter estimate for Black-box processes. IEEE Transactions on Circuits and Systems-I: Regular Papers, vol.53, No.2, 419-430.
  5. Lo, K. M. , Kwon, W. H. (2003), New identification approaches for disturbed models. Automatica.vol.39, No.9, 1627-1634.
  6. Lo, K. M. , Kimura, H. (2003), Recursive Estimation Methods for Discrete Systems. IEEE Trans. On Circruits and Systems.vol.49, NO.6, No.6, 439-446.
  7. Ljung, L. (1985), On the estimation of transfer function. Automaticavol. 21, 677-696.
  8. Ljung, L. (1999), System Identification: Theory for the User. Upper Saddle River,NJ: Prentice-Hall, 1999.
  9. Mershed, R. and Sayed, A. J. (2000), Order-recursive RLS Laguerre adaptive filtering. IEEE Trans. Signal Processing, vol. 48, pp. 3000C3010, July.
  10. Stoica, P., Friedlander, B.and Soderstrom, T.(1987) Optimal Intrumental Variable Multistep Algorithms for Estimation of the AR Parameters of an ARMA Process, Int. J. Control 45(1987), 2083-2107.
  11. Stoica, P. and Jansson, M.(2001) Estimating Optimal Weights for Instrumental Variable Methods. Digital Signal Processing - A Review Journal vol. 11, no. 3, pp. 252-268, Jul. 2001.
  12. Trump, T. (2001), Maximum likelihood trend estimation in exponential noise. IEEE Trans. Signal Processing, vol. 49, pp. 2087C2095, Sept.
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Paper Citation


in Harvard Style

Zhao Y., Lo K. and Kwon W. (2008). IDENTIFICATION OF MULTI-DIMENSIONAL SYSTEM BASED ON A NOVEL CRITERION . In Proceedings of the Fifth International Conference on Informatics in Control, Automation and Robotics - Volume 3: ICINCO, ISBN 978-989-8111-32-6, pages 143-148. DOI: 10.5220/0001492001430148


in Bibtex Style

@conference{icinco08,
author={Yue Zhao and Kueiming Lo and Wook-Hyun Kwon},
title={IDENTIFICATION OF MULTI-DIMENSIONAL SYSTEM BASED ON A NOVEL CRITERION},
booktitle={Proceedings of the Fifth International Conference on Informatics in Control, Automation and Robotics - Volume 3: ICINCO,},
year={2008},
pages={143-148},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0001492001430148},
isbn={978-989-8111-32-6},
}


in EndNote Style

TY - CONF
JO - Proceedings of the Fifth International Conference on Informatics in Control, Automation and Robotics - Volume 3: ICINCO,
TI - IDENTIFICATION OF MULTI-DIMENSIONAL SYSTEM BASED ON A NOVEL CRITERION
SN - 978-989-8111-32-6
AU - Zhao Y.
AU - Lo K.
AU - Kwon W.
PY - 2008
SP - 143
EP - 148
DO - 10.5220/0001492001430148