Hierarchical gradient based and hierarchical least squares based iterative parameter identification for CARARMA systems

Signal Processing

Volumn 97, Issue , 2014, Pages 31-39

  Ding, Feng ^a   Liu, Ximei ^a   Chen, Huibo ^a   Yao, Guoyu ^a  

^a QINGDAO UNIVERSITY OF SCIENCE AND TECHNOLOGY   (China)

Author keywords

Hierarchical identification principle; Iterative algorithm; Least squares; Parameter estimation; Two stage identification

Indexed keywords

AUTOREGRESSIVE MOVING AVERAGE; GRADIENT BASED ITERATIVE ALGORITHMS; HIERARCHICAL IDENTIFICATION PRINCIPLE; ITERATIVE ALGORITHM; ITERATIVE IDENTIFICATION; ITERATIVE PARAMETER ESTIMATION; LEAST SQUARE; TWO-STAGE IDENTIFICATION;

ALGORITHMS; HIERARCHICAL SYSTEMS; PARAMETER ESTIMATION;

ITERATIVE METHODS;

EID: 84887363358     PISSN: 01651684     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.sigpro.2013.10.018     Document Type: Article

References (43)

1. F. Ding System Identification - New Theory and Methods 2013 Science Press Beijing
2. H.P. Li, and Y. Shi Robust H-infinity filtering for nonlinear stochastic systems with uncertainties and Markov delays Automatica 48 1 2012 159 166
3. D.Q. Wang, and F. Ding Least squares based and gradient based iterative identification for Wiener nonlinear systems Signal Process. 91 5 2011 1182 1189
4. F. Ding Decomposition based fast least squares algorithm for output error systems Signal Process. 93 5 2013 1235 1242
5. H. Fang, Y. Shi, and J. Yi On stable simultaneous input and state estimation for discrete-time linear systems Int. J. Adapt. Control Signal Process. 25 8 2011 671 686
6. Y. Shi, and H.Z. Fang Kalman filter-based identification for systems with randomly missing measurements in a network environment Int. J. Control 83 3 2010 538 551
7. F. Ding Combined state and least squares parameter estimation algorithms for dynamic systems Appl. Math. Model. 37 2013 37 10.1016/j.apm.2013.06.007
8. M. Scarpiniti, D. Comminiello, R. Parisi, and A. Uncini Nonlinear spline adaptive filtering Signal Process. 93 4 2013 772 783
9. A. Carini, G.L. Sicuranza, and V.J. Mathews Efficient adaptive identification of linear-in-the-parameters nonlinear filters using periodic input sequences Signal Process. 93 5 2013 1210 1220
10. H. Zhang, Y. Shi, A.S. Mehr, and H.N. Huang Robust FIR equalization for time-varying communication channels with intermittent observations via an LMI approach Signal Process. 91 7 2011 1651 1658
11. I.J. Umoh, and T. Ogunfunmi An affine projection-based algorithm for identification of nonlinear Hammerstein systems Signal Process. 90 6 2010 2020 2030
12. G. Mileounis, and N. Kalouptsidis A sparsity driven approach to cumulant based identification and order determination Signal Process. 93 7 2013 1892 1899
13. M. Dehghan, and M. Hajarian Matrix equations over (R, S)-symmetric and (R, S)-skew symmetric matrices Comput. Math. Appl. 59 11 2010 3583 3594
14. D.Q. Wang, and Y.Y. Chu Auxiliary model-based RELS and MI-ELS algorithms for Hammerstein OEMA systems Comput. Math. Appl. 59 9 2010 3092 3098
15. F. Ding, G. Liu, and X.P. Liu Parameter estimation with scarce measurements Automatica 47 8 2011 1646 1655
16. F. Ding Hierarchical multi-innovation stochastic gradient algorithm for Hammerstein nonlinear system modeling Appl. Math. Model. 37 4 2013 1694 1704
17. H.Q. Han, and L. Xie Hierarchical least squares based iterative identification for multivariable systems with moving average noises Math. Comput. Model. 51 9-10 2010 1213 1220
18. Z.N. Zhang, F. Ding, and X.G. Liu Hierarchical gradient based iterative parameter estimation algorithm for multivariable output error moving average systems Comput. Math. Appl. 61 3 2011 672 682
19. F. Ding, G. Liu, and X.P. Liu Partially coupled stochastic gradient identification methods for non-uniformly sampled systems IEEE Trans. Autom. Control 55 8 2010 1976 1981
20. F. Ding Coupled-least-squares identification for multivariable systems IET Control Theory Appl. 7 1 2013 68 79
21. Y. Zhang Unbiased identification of a class of multi-input single-output systems with correlated disturbances using bias compensation methods Math. Comput. Model. 53 9-10 2011 1810 1819
22. Y. Zhang, and G.M. Cui Bias compensation methods for stochastic systems with colored noise Appl. Math. Model. 35 4 2011 1709 1716
23. F. Ding, P.X. Liu, and G. Liu Gradient based and least-squares based iterative identification methods for OE and OEMA systems Digit. Signal Process. 20 3 2010 664 677
24. J. Vörös Modeling and parameter identification of systems with multi-segment piecewise-linear characteristics IEEE Trans. Autom. Control 47 1 2002 184 188 (Pubitemid 34504342)
25. J. Vörös Recursive identification of Hammerstein systems with discontinuous nonlinearities containing dead-zones IEEE Trans. Autom. Control 48 12 2003 2203 2206
26. J. Vörös Parameter identification of Wiener systems with multisegment piecewise-linear nonlinearities Syst. Control Lett. 56 2 2007 99 105 (Pubitemid 44854240)
27. Y.J. Liu, J. Sheng, and R.F. Ding Convergence of stochastic gradient estimation algorithm for multivariable ARX-like systems Comput. Math. Appl. 59 8 2010 2615 2627
28. Y.S. Xiao, and N. Yue Parameter estimation for nonlinear dynamical adjustment models Math. Comput. Model. 54 5-6 2011 1561 1568
29. G.Y. Yao, and R.F. Ding Two-stage least squares based iterative identification algorithm for controlled autoregressive moving average (CARMA) systems Comput. Math. Appl. 63 5 2012 975 984
30. H.B. Chen, G.Y. Yao, R. Ding, A decomposition based least squares iterative parameter estimation algorithm for controlled autoregressive autoregressive moving average systems, in: Third International Conference on Information Science and Technology, Yangzhou, China, March 23-25, 2013, pp. 308-312.
31. F. Ding, Y.J. Liu, and B. Bao Gradient based and least squares based iterative estimation algorithms for multi-input multi-output systems Proc. Inst. Mech. Eng. Part I J. Syst. Control Eng. 226 1 2012 43 55
32. D.Q. Wang, and F. Ding Input-output data filtering based recursive least squares parameter estimation for CARARMA systems Digit. Signal Process. 20 4 2010 991 999
33. 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 Appl. 7 2 2013 176 184
34. F. Ding, and H.H. Duan Two-stage parameter estimation algorithms for Box-Jenkins systems IET Signal Process. 7 8 2013 646 654
35. H.H. Duan, J. Jia, and R.F. Ding Two-stage recursive least squares parameter estimation algorithm for output error models Math. Comput. Model. 55 3-4 2012 1151 1159
36. F. Ding Two-stage least squares based iterative estimation algorithm for CARARMA system modeling Appl. Math. Model. 37 7 2013 4798 4808
37. D.Q. Wang, G.W. Yang, and R.F. Ding Gradient-based iterative parameter estimation for Box-Jenkins systems Comput. Math. Appl. 60 5 2010 1200 1208
38. Y.J. Liu, and D.Q. Wang Least squares based iterative algorithms for identifying Box-Jenkins models with finite measurement data Digit. Signal Process. 20 5 2010 1458 1467
39. G.H. Golub, and C.F. Van Loan Matrix Computations 3rd ed. 1996 Johns Hopkins University Press Baltimore, MD
40. D.Q. Wang, and F. Ding Hierarchical least squares estimation algorithm for Hammerstein-Wiener systems IEEE Signal Process. Lett. 19 12 2012 825 828
41. D.Q. Wang, F. Ding, and Y.Y. Chu Data filtering based recursive least squares algorithm for Hammerstein-systems using the key-term separation principle Information Sciences 222 2013 203 212
42. F. Ding, J.X. Ma, and Y.S. Xiao Newton iterative identification for a class of output nonlinear systems with moving average noises Nonlinear Dynamics 74 1-2 2013 21 30
43. D.Q. Wang, F. Ding, X.M. Liu, Least squares algorithm for an input nonlinear system with a dynamic subspace state space model, Nonlinear Dynamics 2014, 10.1007/s11071-013-1048-8.