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CLC number: TP273
On-line Access: 2024-08-27
Received: 2023-10-17
Revision Accepted: 2024-05-08
Crosschecked: 2014-01-15
Cited: 2
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Stochastic gradient algorithm for a dual-rate Box-Jenkins model based on auxiliary model and FIR model
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Jing Chen, Rui-feng Ding. Stochastic gradient algorithm for a dual-rate Box-Jenkins model based on auxiliary model and FIR model[J]. Journal of Zhejiang University Science C, 2014, 15(2): 147-152.
@article{title="Stochastic gradient algorithm for a dual-rate Box-Jenkins model based on auxiliary model and FIR model",
author="Jing Chen, Rui-feng Ding",
journal="Journal of Zhejiang University Science C",
volume="15",
number="2",
pages="147-152",
year="2014",
publisher="Zhejiang University Press & Springer",
doi="10.1631/jzus.C1300072"
}
%0 Journal Article
%T Stochastic gradient algorithm for a dual-rate Box-Jenkins model based on auxiliary model and FIR model
%A Jing Chen
%A Rui-feng Ding
%J Journal of Zhejiang University SCIENCE C
%V 15
%N 2
%P 147-152
%@ 1869-1951
%D 2014
%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.C1300072
TY - JOUR
T1 - Stochastic gradient algorithm for a dual-rate Box-Jenkins model based on auxiliary model and FIR model
A1 - Jing Chen
A1 - Rui-feng Ding
J0 - Journal of Zhejiang University Science C
VL - 15
IS - 2
SP - 147
EP - 152
%@ 1869-1951
Y1 - 2014
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/jzus.C1300072
Abstract: Based on the work in Ding and Ding (2008), we develop a modified stochastic gradient (SG) parameter estimation algorithm for a dual-rate box-Jenkins model by using an auxiliary model. We simplify the complex dual-rate box-Jenkins model to two finite impulse response (FIR) models, present an auxiliary model to estimate the missing outputs and the unknown noise variables, and compute all the unknown parameters of the system with colored noises. Simulation results indicate that the proposed method is effective.
[1]BuHamra, S., Smaoui, N., Gabr, M., 2003. The Box-Jenkins analysis and neural networks: prediction and time series modelling. Appl. Math. Model., 27(10):805-815.
[2]Cattivelli, F.S., Lopes, C.G., Sayed, A.H., 2008. Diffusion recursive least-squares for distributed estimation over adaptive networks. IEEE Trans. Signal Process., 56(5):1865-1877.
[3]Chen, J., 2014. Several gradient parameter estimation algorithms for dual-rate sampled systems. J. Frank. Inst., 351(1):543-554.
[4]Chen, J., Ding, F., 2011. Modified stochastic gradient algorithms with fast convergence rates. J. Vibr. Contr., 17(9):1281-1286.
[5]Chen, J., Lu, X.L., Ding, R.F., 2012. Parameter identification of systems with preload nonlinearities based on the finite impulse response model and negative gradient search. Appl. Math. Comput., 219(5):2498-2505.
[6]Deboucha, A., Taha, Z., 2010. Identification and control of a small-scale helicopter. J. Zhejiang Univ.-Sci. A (Appl. Phys. & Eng.), 11(12):978-985.
[7]Ding, F., 2013a. Coupled-least-squares identification for multivariable systems. IET Contr. Theory Appl., 7(1):68-79.
[8]Ding, F., 2013b. Hierarchical multi-innovation stochastic gradient algorithm for Hammerstein nonlinear system modeling. Appl. Math. Model., 37(4):1694-1704.
[9]Ding, F., 2013c. Decomposition based fast least squares algorithm for output error systems. Signal Process., 93(5):1235-1242.
[10]Ding, F., 2014. Combined state and least squares parameters estimation algorithms for dynamic systems. Appl. Math. Model., 38(1):403-412.
[11]Ding, F., Liu, X.G., Chu, J., 2013. Gradient-based and least-squares-based iterative algorithms for Hammerstein systems using the hierarchical identification principle. IET Contr. Theory Appl., 7(2):176-184.
[12]Ding, J., Ding, F., 2008. The residual based extended least squares identification method for dual-rate systems. Comput. Math. Appl., 56(6):1479-1487.
[13]Forssell, U., Ljung, L., 2000. Identification of unstable systems using output error and Box-Jenkins model structures. IEEE Trans. Autom. Contr., 45(1):137-141.
[14]Kadu, S.C., Bhushan, M., Gudi, R., 2008. Optimal sensor network design for multirate systems. J. Proc. Contr., 18(6):594-609.
[15]Liu, Y.J., Xiao, Y.S., Zhao, X.L., 2009. Multi-innovation stochastic gradient algorithm for multiple-input single-output systems using the auxiliary model. Appl. Math. Comput., 215(4):1477-1483.
[16]Liu, Y.J., Yu, L., Ding, F., 2010a. Multi-innovation extended stochastic gradient algorithm and its performance analysis. Circ. Syst. Signal Process., 29(4):649-667.
[17]Liu, Y.J., Wang, D.Q., Ding, F., 2010b. Least-squares based iterative algorithms for identifying Box-Jenkins models with finite measurement data. Digit. Signal Process., 20(5):1458-1467.
[18]Nakamori, S., Hermoso-Carazo, A., Linares-Pérez, J., 2007. Suboptimal estimation of signals from uncertain observations using approximations of mixtures. Digit. Signal Process., 17(1):4-16.
[19]Sägfors, M.F., Toivonen, H.T., 1997. H∞ and LQG control of asynchronous sampled-data systems. Automatica, 33(9):1663-1668.
[20]Shi, Y., Ding, F., Chen, T., 2006. Multirate crosstalk identification in xDSL systems. IEEE Trans. Commun., 54(10):1878-1886.
[21]Vörös, J., 2010. Modeling and identification of systems with backlash. Automatica, 46(2):369-374.
[22]Wang, D.Q., Yang, G.W., Ding, R.F., 2010. Gradient-based iterative parameter estimation for Box-Jenkins systems. Comput. Math. Appl., 60(5):1200-1208.
[23]Wu, P., Yang, C.J., Song, Z.H., 2009. Subspace identification for continuous-time errors-in-variables model from sampled data. J. Zhejiang Univ.-Sci. A (Appl. Phys. & Eng.), 10(8):1177-1186.
[24]Zong, C.F., Song, P., Hu, D., 2011. Estimation of vehicle states and tire-road friction using parallel extended Kalman filtering. J. Zhejiang Univ.-Sci. A (Appl. Phys. & Eng.), 12(6):446-452.
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