Time-varying time-delay estimation for nonlinear systems using neural networks

Yonghong Tan

International Journal of Applied Mathematics and Computer Science (2004)

  • Volume: 14, Issue: 1, page 63-68
  • ISSN: 1641-876X

Abstract

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Nonlinear dynamic processes with time-varying time delays can often be encountered in industry. Time-delay estimation for nonlinear dynamic systems with time-varying time delays is an important issue for system identification. In order to estimate the dynamics of a process, a dynamic neural network with an external recurrent structure is applied in the modeling procedure. In the case where a delay is time varying, a useful way is to develop on-line time-delay estimation mechanisms to track the time-delay variation. In this paper, two schemes called direct and indirect time-delay estimators are proposed. The indirect time-delay estimator considers the procedure of time-delay estimation as a nonlinear programming problem. On the other hand, the direct time-delay estimation scheme applies a neural network to construct a time-delay estimator to track the time-varying time-delay. Finally, a numerical example is considered for testing the proposed methods.

How to cite

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Tan, Yonghong. "Time-varying time-delay estimation for nonlinear systems using neural networks." International Journal of Applied Mathematics and Computer Science 14.1 (2004): 63-68. <http://eudml.org/doc/207680>.

@article{Tan2004,
abstract = {Nonlinear dynamic processes with time-varying time delays can often be encountered in industry. Time-delay estimation for nonlinear dynamic systems with time-varying time delays is an important issue for system identification. In order to estimate the dynamics of a process, a dynamic neural network with an external recurrent structure is applied in the modeling procedure. In the case where a delay is time varying, a useful way is to develop on-line time-delay estimation mechanisms to track the time-delay variation. In this paper, two schemes called direct and indirect time-delay estimators are proposed. The indirect time-delay estimator considers the procedure of time-delay estimation as a nonlinear programming problem. On the other hand, the direct time-delay estimation scheme applies a neural network to construct a time-delay estimator to track the time-varying time-delay. Finally, a numerical example is considered for testing the proposed methods.},
author = {Tan, Yonghong},
journal = {International Journal of Applied Mathematics and Computer Science},
keywords = {estimation; neural networks; nonlinear systems; time delay; modelling},
language = {eng},
number = {1},
pages = {63-68},
title = {Time-varying time-delay estimation for nonlinear systems using neural networks},
url = {http://eudml.org/doc/207680},
volume = {14},
year = {2004},
}

TY - JOUR
AU - Tan, Yonghong
TI - Time-varying time-delay estimation for nonlinear systems using neural networks
JO - International Journal of Applied Mathematics and Computer Science
PY - 2004
VL - 14
IS - 1
SP - 63
EP - 68
AB - Nonlinear dynamic processes with time-varying time delays can often be encountered in industry. Time-delay estimation for nonlinear dynamic systems with time-varying time delays is an important issue for system identification. In order to estimate the dynamics of a process, a dynamic neural network with an external recurrent structure is applied in the modeling procedure. In the case where a delay is time varying, a useful way is to develop on-line time-delay estimation mechanisms to track the time-delay variation. In this paper, two schemes called direct and indirect time-delay estimators are proposed. The indirect time-delay estimator considers the procedure of time-delay estimation as a nonlinear programming problem. On the other hand, the direct time-delay estimation scheme applies a neural network to construct a time-delay estimator to track the time-varying time-delay. Finally, a numerical example is considered for testing the proposed methods.
LA - eng
KW - estimation; neural networks; nonlinear systems; time delay; modelling
UR - http://eudml.org/doc/207680
ER -

References

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  1. Balestrino A., Verona F. and Landi A. (1988): On-line process estimation by ANNs and Smith controller design. -IEE Proc., Pt. D. Contr. Theory Appl., Vol. 145, No. 2, pp. 231-235. 
  2. Ching P.C., So H.C. and Wu S.Q. (1999): On wavelet denoising and its applications to time delay estimation.- IEEE Trans. Signal Process., Vol. 47, No. 10, pp. 2879-2882. Zbl1075.94510
  3. Cybenko G. (1989): Approximation by superposition of a sigmoidal function. - Math. Contr. Signal Syst., Vol. 2, No. 4, pp. 303-314. Zbl0679.94019
  4. Lim T.J. and Macleod M.D. (1995): Adaptive algorithm for joint time-delay estimation and IIR filtering. -IEEE Trans. Signal Process., Vol. 43, No. 4, pp. 841-851. 
  5. Reed F., Feintuch P. and Bershad N. (1981): Time-delay estimation using the LMS adaptive filter-static behavior; dynamic behavior. - IEEE Trans. Acoust. Speech Signal Process., Vol. 29, No. 3, pp. 561-576. 
  6. Shor G. and Messer H. (1997): Performance evaluation of time-delay estimation in non-Gaussian conditions. - Proc. IEEE Signal Processing Workshop Higher-Order Statistics, Banff, Canada, pp. 20-24. 
  7. Teng F.C. and Sirisena H.R. (1988): Self-tuning PID controllers for dead time processes. - IEEE Trans. Ind. Electron., Vol. 35, No. 1, pp. 119-125. 

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