Least squares based iterative algorithms for identifying Box-Jenkins models with finite measurement data

Digital Signal Processing: A Review Journal

Volumn 20, Issue 5, 2010, Pages 1458-1467

  Liu, Yanjun ^a   Wang, Dongqing ^b   Ding, Feng ^a  

^a JIANGNAN UNIVERSITY   (China)

^b QINGDAO UNIVERSITY   (China)

Author keywords

Box Jenkins models; Gauss Seidel iterations; Jacobi iterations; Least squares; Parameter estimation; Recursive identification

Indexed keywords

ITERATIVE METHODS;

BOX-JENKINS MODELS; GAUSS SEIDEL ITERATION; JACOBI ITERATION; LEAST SQUARE; RECURSIVE IDENTIFICATION;

PARAMETER ESTIMATION;

EID: 77954174851     PISSN: 10512004     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.dsp.2010.01.004     Document Type: Article

References (17)

1. G.H. Golub, C.F. Van Loan, Matrix Computations, 3rd ed., Johns Hopkins University Press, Baltimore, MD, 1996.
2. F. Ding, T. Chen, Gradient based iterative algorithms for solving a class of matrix equations, IEEE Trans. Automat. Control 50 (8) (2005) 1216-1221. (Pubitemid 41410122)
3. F. Ding, P.X. Liu, J. Ding, Iterative solutions of the generalized Sylvester matrix equations by using the hierarchical identification principle, Appl. Math. Comput. 197 (1) (2008) 41-50. (Pubitemid 351215477)
4. F. Ding, T. Chen, Iterative least squares solutions of coupled Sylvester matrix equations, Syst. Control Lett. 54 (2) (2005) 95-107.
5. A. Kilicman, Z.Al. Zhour, Vector least-squares solutions for coupled singular matrix equations, J. Comput. Appl. Math. 206 (2) (2007) 1051-1069. (Pubitemid 46856057)
6. Z.Al. Zhour, A. Kilicman, Some new connections between matrix products for partitioned and non-partitioned matrices, Comput. Math. Appl. 54 (6) (2007) 763-784. (Pubitemid 47135621)
7. G.C. Goodwin, K.S. Sin, Adaptive Filtering Prediction and Control, Prentice-Hall, Englewood Cliffs, NJ, 1984.
8. L. Ljung, System Identification: Theory for the User, 2nd ed., Prentice-Hall, Englewood Cliffs, NJ, 1999.
9. F. Ding, Y. Shi, T. Chen, Performance analysis of estimation algorithms of non-stationary ARMA processes, IEEE Trans. Signal Process. 54 (3) (2006) 1041-1053. (Pubitemid 43280579)
10. K.S. Narendra, P.G. Gallman, An iterative method for the identification of nonlinear systems using a Hammerstein model, IEEE Trans. Automat. Control 11 (3) (1966) 546-550.
11. E.W. Bai, D. Li, Convergence of the iterative Hammerstein system identification algorithm, IEEE Trans. Automat. Control 49 (11) (2004) 1929-1940.
12. Y. Liu, E.W. Bai, Iterative identification of Hammerstein systems, Automatica 43 (2) (2007) 346-354. (Pubitemid 46037595)
13. F. Ding, P.X. Liu, G. Liu, Gradient based and least-squares based iterative identification methods for OE and OEMA systems, Digital Signal Process. 20 (3) (2010) 664-677.
14. D.Q. Wang, J.Y. Dai, F. Ding, Gradient-based iterative parameter estimation for Box-Jenkins systems with finite measurement data, in: 48th IEEE Conference on Decision and Control and 28th Chinese Control Conference, Shanghai, China, December 16-18, 2009, pp. 239-243.
15. Y. J Liu, Y.S. Xiao, X.L. Zhao, Multi-innovation stochastic gradient algorithm for multiple-input single-output systems using the auxiliary model, Appl. Math. Comput. 215 (4) (2009) 1477-1483.
16. L.L. Han, F. Ding, Multi-innovation stochastic gradient algorithms for multi-input multi-output systems, Digital Signal Process. 19 (4) (2009) 545-554.
17. Y.J. Liu, L. Xie, F. Ding, An auxiliary model based recursive least squares parameter estimation algorithm for non-uniformly sampled multirate systems, J. Syst. Control Eng. 223 (4) (2009) 445-454.