Least squares based iterative identification algorithm for output error autoregressive systems using the decomposition technique and the data filtering

2013 IEEE 3rd International Conference on Information Science and Technology, ICIST 2013

Volumn , Issue , 2013, Pages 195-200

  Zhang, Wenge ^a   Ding, Rui ^a  

^a JIANGNAN UNIVERSITY   (China)

Author keywords

[No Author keywords available]

Indexed keywords

INFORMATION SCIENCE; ITERATIVE METHODS;

AUTOREGRESSIVE SYSTEMS; COMPUTATIONAL BURDEN; DATA FILTERING; DECOMPOSITION TECHNIQUE; IDENTIFICATION MODEL; ITERATIVE ALGORITHM; ITERATIVE IDENTIFICATION; OUTPUT ERROR MODELS;

ALGORITHMS;

EID: 84898981894     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICIST.2013.6747534     Document Type: Conference Paper

References (66)

1. F. Ding, System Identification - New Theory and Method, Beijing: Science Press, 2013.
2. F. Ding, " System identification - Part A: Introduction to the identification", Journal of Nanjing University of Information Science and Technology (Natural Science Edition), vol. 3, no. 1, pp. 1-22, 2011.
3. F. Ding, " System identification - Part B: Basic models for system description", Journal of Nanjing University of Information Science and Technology (Natural Science Edition), vol. 3, no. 2, pp. 97-117, 2011.
4. F. Ding, " System identification - Part C: Identification accuracy and basic problems", Journal of Nanjing University of Information Science and Technology (Natural Science Edition), vol. 3, no. 3, pp. 193-226, 2011.
5. F. Ding, " System identification - Part D: Auxiliary model identification idea and methods", Journal of Nanjing University of Information Science and Technology (Natural Science Edition), vol. 3, no. 4, pp. 289-318, 2011.
6. F. Ding, " System identification - Part E: Iterative search principle and identification methods", Journal of Nanjing University of Information Science and Technology (Natural ScienceEdition), vol. 3, no. 6, pp. 481-510, 2011.
7. F. Ding, " System identification - Part F: Multi-innovation identification theory and methods", Journal of Nanjing University of Information Science and Technology (Natural Science Edition), vol. 4, no. 1, pp. 1-28, 2012.
8. F. Ding, " System identification - Part G: Hierarchical identification principle and methods", Journal of Nanjing University of Information Science and Technology (Natural Science Edition), vol. 4, no. 2, pp. 97-124, 2012.
9. F. Ding, " System identification - Part H: Coupling identification concept and methods", Journal of Nanjing University of Information Science and Technology (Natural Science Edition), vol. 4, no. 3, pp. 193-212, 2012.
10. F. Ding, " Computational efficiency of the identification methods - Part A: Recursive algorithms", Journal of Nanjing University of Information Science and Technology (Natural Science Edition), vol. 4, no. 4, pp. 289-300, 2012.
11. F. Ding, " Computational efficiency of the identification methods - Part B: Iterative algorithms", Journal of Nanjing University of Information Science and Technology (Natural Science Edition), vol. 4, no. 5, pp. 385-401, 2012.
12. F. Ding, " Computational efficiency of the identification methods - Part C: Coupled information vector algorithms", Journal of Nanjing University of Information Science and Technology (Natural Science Edition), vol. 4, no. 6, pp. 481-495, 2012.
13. T. Soderstrom, M. Hong, J. Schoukens, and R. Pintelon, " Accuracy analysis of time domain maximum likelihood method and sample maximum likelihood method for errors-in-variables and output error identification", Automatica, vol. 46, no. 4, pp. 721-727, 2010.
14. Z. N. Zhang, F. Ding, and X. G. Liu, " Hierarchical gradient based iterative parameter estimation algorithm for multivariable output error moving average systems", Computers & Mathematics with Applications, vol. 61, no. 3, pp. 672-682, 2011.
15. D. Q. Wang, " Least squares-based recursive and iterative estimation for output error moving average (OEMA) systems using data filtering", IET Control Theory and Applications, vol. 5, no. 14, pp. 1648-1657, 2011.
16. F. Ding, X. P. Liu, and G. Liu, " Gradient based and least-squares based iterative identification methods for OE and OEMA systems", Digital Signal Processing, vol. 20, no. 3, pp. 664-677, 2010.
17. Y. J. Liu, D. Q. Wang, and F. Ding, " Least-squares based iterative algorithms for identifying Box-Jenkins models with finite measurement data", Digital Signal Processing, vol. 20, no. 5, pp. 1458-1467, 2010.
18. Y. Zhang and G. M. Cui, " Bias compensation methods for stochastic systems with colored noise", Applied Mathematical Modelling, vol. 35, no. 4, pp. 1709-1716, 2011.
19. Y. Liu and E. -W. Bai, " Iterative identification of Hammerstein systems", Automatica, vol. 43, no. 2, pp. 346-354, 2007.
20. B. Bao, Y. Q. Xu, J. Sheng, and R. F. Ding, " Least squares based iterative parameter estimation algorithm for multivariable controlled ARMA system modelling with finite measurement data", Mathematical and Computer Modelling, vol. 53, no. 9-10, pp. 1664-1669, 2011.
21. X. G. Liu and J. Lu, " Least squares based iterative identification for a class of multirate systems", Automatica, vol. 46, no. 3, pp. 549-554, 2010.
22. G. Y. Yao and R. F. Ding, " Two-stage least squares based iterative estimation algorithm for controlled autoregressive moving average (CARMA) systems", Computers & Mathematics with Applications, vol. 63, no. 5, pp. 975-984, 2012.
23. H. H. Duan, J. Jia, and R. F. Ding, " Two-stage recursive least squares parameter estimation algorithm for output error models", Mathmatical and Computer Modelling, vol. 55, no. 3-4, pp. 1151-1159, 2012.
24. S. J. Wang and R. Ding, " Three-stage recursive least squares parameter estimation for controlled autoregressive autoregressive systems", Applied Mathematical Modelling, vol. 36, no. x, pp. XXX-XXX, 2013.
25. W. G. Zhang and R. F. Ding, " Decomposition based least squares iterative estimation for output error moving average systems", Engineering Computations, vol. 30, no. x, pp. XXX-XXX, 2013.
26. H. Q. Han, L. Xie, F. Ding, and X. G. Liu, " Hierarchical least squares based iterative identification for multivariable systems with moving average noises", Mathematical and Computer Modelling, vol. 51, no. 9-10, pp. 1213-1220, 2010.
27. Y. J. Liu, Y. S. Xiao, and X. L. Zhao, " Multi-innovation stochastic gradient algorithm for multiple-input single-output systems using the auxiliary model" , Applied Mathematics and Computation, vol. 215, no. 4, pp. 1477-1483, 2009.
28. D. Q. Wang and F. Ding, " Hierarchical least squares estimation algorithm for Hammerstein-Wiener systems" , IEEE Signal Processing Letters, vol. 19, no. 12, pp. 825-828, 2012.
29. F. Ding, X. P. Liu, and G. Liu, " Multi-innovation least squares identification for system modeling" , IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, vol. 40, no. 3, pp. 767-778, 2010.
30. F. Ding, " Several multi-innovation identification methods" , Digital Signal Processing, vol. 20, no. 4, pp. 1027-1039, 2010.
31. Y. S. Xiao, G. L. Song, and et al, " Multi-innovation stochastic gradient parameter estimation for input nonlinear controlled autoregressive models" , International Journal of Control, Automation, and Systems, vol. 10, no. 3, pp. 639-643, 2012.
32. F. Ding, X. P. Liu, and G. Liu, " Auxiliary model based multi-innovation extended stochastic gradient parameter estimation with colored measurement noises" , Signal Processing, vol. 89, no. 10, pp. 1883-1890, 2009.
33. F. Ding, " Hierarchical multi-innovation stochastic gradient algorithm for Hammerstein nonlinear system modeling" , Applied Mathematical Modelling, vol. 37, no. 4, pp. 1694-1704, 2013.
34. L. L. Han and F. Ding, " Multi-innovation stochastic gradient algorithms for multi-input multi-output systems" , Digital Signal Processing, vol. 19, no. 4, pp. 545-554, 2009.
35. J. Chen, L. X. Lv, and R. F. Ding, " Multi-innovation stochastic gradient algorithms for dual-rate sampled systems with preload nonlinearity" , Applied Mathematics Letters, vol. 26, no. 1, pp. 124-129, 2013.
36. F. Ding, " Two-stage least squares based iterative estimation algorithm for CARARMA system modeling" , Applied Mathematical Modelling, vol. 37, no. 7, pp. 4798-4808, 2013.
37. F. Ding, " Decomposition based fast least squares algorithm for output error systems" , Signal Processing, vol. 93, no. 5, pp. 1235-1242, 2013.
38. L. Xie, H. Z. Yang, and et al, " Recursive least squares parameter estimation for non-uniformly sampled systems based on the data filtering" , Mathematical and Computer Modelling, vol. 54, no. 1-2, pp. 315-324, 2011.
39. D. Q. Wang, " Least squares based recursive and iterative estimation for OEMA systems using the data filtering" , IET Control theory and Applications, vol. 5, no. 14, pp. 1648-1657, 2011.
40. Y. S. Xiao and N. Yue, " Parameter estimation for nonlinear dynamical adjustment models" , Mathematical and Computer Modelling, vol. 54, no. 5-6, pp. 1561-1568, 2011.
41. L. Xie and H. Z. Yang, " Gradient based iterative identification for non-uniform sampled output error systems" , Journal of Vibration and Control, vol. 17, no. 3, pp. 471-478, 2011.
42. J. H. Li, R. F. Ding, and Y. Yang, " Iterative parameter identification methods for nonlinear functions" , Applied Mathematical Modelling, vol. 36, no. 6, pp. 2739-2750, 2012.
43. F. Ding, " Coupled-least-squares identification for multivariable systems" , IET Control Theory and Applications, vol. 7, no. x, 2013. doi: 10. 1049/iet-cta. 2012. 0171
44. F. Ding, X. G. Liu, and J. Chu, " Gradient-based and least-squares-based iterative algorithms for Hammerstein systems using the hierarchical identification principle" , IET Control Theory and Applications, vol. 7, no. x, 2013. doi:10. 1049/iet-cta. 2012. 0313
45. D. Q. Wang and F. Ding, " Least squares based and gradient based iterative identification for Wiener nonlinear systems" , Signal Processing, vol. 91, no. 5, pp. 1182-1189, 2011.
46. F. Ding, X. P. Liu, and G. Liu, " Identification methods for Hammerstein nonlinear systems" , Digital Signal Processing, vol. 21, no. 2, pp. 215-238, 2011.
47. J. Ding, Y. Shi, and et al, " A modified stochastic gradient based parameter estimation algorithm for dual-rate sampled-data systems" , Digital Signal Processing, vol. 20, no. 4, pp. 1238-1249, 2010.
48. J. Ding, F. Ding, and et al, " Hierarchical least squares identification for linear SISO systems with dual-rate sampled-data" , IEEE Transactions on Automatic Control, vol. 56, no. 11, pp. 2677-2683, 2011.
49. F. Ding, G. Liu, and X. P. Liu, " Partially coupled stochastic gradient identification methods for non-uniformly sampled systems" , IEEE Transactions on Automatic Control, vol. 55, no. 8, pp. 1976-1981, 2010.
50. J. H. Li, " Parameter estimation for Hammerstein CARARMA systems based on the Newton iteration" , Applied Mathematics Letters, vol. 26, no. 1, pp. 91-96, 2013.
51. Y. Zhang, " Unbiased identification of a class of multi-input single-output systems with correlated disturbances using bias compensation methods" , Mathematical and Computer Modelling, vol. 53, no. 9-10, pp. 1810-1819, 2011.
52. D. Q. Wang, Y. Y. Chu, and et al, " Auxiliary model-based RELS and MIELS algorithms for Hammerstein OEMA systems" , Computers Mathematics with Applications, vol. 59, no. 9, pp. 3092-3098, 2010.
53. Y. J. Liu, L. Yu, and et al, " Multi-innovation extended stochastic gradient algorithm and its performance analysis" , Circuits, Systems and Signal Processing, vol. 29, no. 4, pp. 649-667, 2010.
54. Y. S. Xiao, Y. Zhang, J. Ding, and J. Y. Dai, " The residual based interactive least squares algorithms and simulation studies" , Computers & Mathematics with Applications, vol. 58, no. 6, pp. 1190-1197, 2009.
55. D. Q. Wang, G. W. Yang, and R. F. Ding, " Gradient-based iterative parameter estimation for Box-Jenkins systems" , Computers & Mathematics with Applications, vol. 60, no. 5, pp. 1200-1208, 2010.
56. L. Y. Wang, L. Xie, and X. F. Wang, " The residual based interactive stochastic gradient algorithms for controlled moving average models" , Applied Mathematics and Computation, vol. 211, no. 2, pp. 442-449, 2009.
57. J. Chen, Y. Zhang, and R. F. Ding, " Auxiliary model based multiinnovation algorithms for multivariable nonlinear systems" , Mathematical and Computer Modelling, vol. 52, no. 9-10, pp. 1428-1434, 2010.
58. F. Ding, G. Liu, and X. P. Liu, " Parameter estimation with scarce measurements" , Automatica, vol. 47, no. 8, pp. 1646-1655, 2011.
59. Y. J. Liu, L. Xie, and et al, " An auxiliary model based recursive least squares parameter estimation algorithm for non-uniformly sampled multirate systems" , Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering, vol. 223, no. 4, pp. 445-454, 2009.
60. Y. J. Liu, J. Sheng, and R. F. Ding, " Convergence of stochastic gradient estimation algorithm for multivariable ARX-like systems" , Computers & Mathematics with Applications, vol. 59, no. 8, pp. 2615-2627, 2010.
61. J. Ding, L. L. Han, and X. M. Chen, " Time series AR modeling with missing observations based on the polynomial transformation" , Mathematical and Computer Modelling, vol. 51, no. 5-6, pp. 527-536, 2010.
62. D. Q. Wang, R. Ding, and X. Z. Dong, " Iterative parameter estimation for a class of multivariable systems based on the hierarchical identification principle and the gradient search" , Circuits, Systems and Signal Processing, vol. 31, no. 6, pp. 2167-2177, 2012.
63. F. Ding, T. Chen, and L. Qiu, " Bias compensation based recursive least squares identification algorithm for MISO systems" , IEEE Transactions on Circuits and Systems II: Express Briefs, vol. 53, no. 5, pp. 349-353, 2006.
64. L. Xie, Y. J. Liu, and et al, " Modeling and identification for nonuniformly periodically sampled-data systems" , IET Control Theory and Applications, vol. 4, no. 5, pp. 784-794, 2010.
65. F. Ding, H. Z. Yang, and F. Liu, " Performance analysis of stochastic gradient algorithms under weak conditions" , Science in China Series F-Information Sciences, vol. 51, no. 9, pp. 1269-1280, 2008.
66. F. Ding, L. Qiu, and T. Chen, " Reconstruction of continuous-time systems from their non-uniformly sampled discrete-time systems" , Automatica, vol. 45, no. 2, pp. 324-332, 2009.