Geodesic
Time-varying time-delay estimation for nonlinear systems using neural networks
International Journal of Applied Mathematics and Computer Science, Tome 14 (2004) no. 1, pp. 63-68.

Voir la notice de l'article provenant de la source Library of Science

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.
Keywords: modelling, time delay, nonlinear systems, neural networks, estimation
Mots-clés : modelowanie procesu, opóźnienie czasowe, układ nieliniowy, sieć neuronowa 
@article{IJAMCS_2004_14_1_a7,
     author = {Tan, Y.},
     title = {Time-varying time-delay estimation for nonlinear systems using neural networks},
     journal = {International Journal of Applied Mathematics and Computer Science},
     pages = {63--68},
     publisher = {mathdoc},
     volume = {14},
     number = {1},
     year = {2004},
     language = {en},
     url = {http://geodesic.mathdoc.fr/item/IJAMCS_2004_14_1_a7/}
}
TY  - JOUR
AU  - Tan, Y.
TI  - Time-varying time-delay estimation for nonlinear systems using neural networks
JO  - International Journal of Applied Mathematics and Computer Science
PY  - 2004
SP  - 63
EP  - 68
VL  - 14
IS  - 1
PB  - mathdoc
UR  - http://geodesic.mathdoc.fr/item/IJAMCS_2004_14_1_a7/
LA  - en
ID  - IJAMCS_2004_14_1_a7
ER  - 
%0 Journal Article
%A Tan, Y.
%T Time-varying time-delay estimation for nonlinear systems using neural networks
%J International Journal of Applied Mathematics and Computer Science
%D 2004
%P 63-68
%V 14
%N 1
%I mathdoc
%U http://geodesic.mathdoc.fr/item/IJAMCS_2004_14_1_a7/
%G en
%F IJAMCS_2004_14_1_a7
Tan, Y. Time-varying time-delay estimation for nonlinear systems using neural networks. International Journal of Applied Mathematics and Computer Science, Tome 14 (2004) no. 1, pp. 63-68. http://geodesic.mathdoc.fr/item/IJAMCS_2004_14_1_a7/

[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.

[3] Cybenko G. (1989): Approximation by superposition of a sigmoidal function. — Math. Contr. Signal Syst., Vol. 2, No. 4, pp. 303–314.

[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 ProcessingWorkshop 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.