Identification methods for Wiener nonlinear systems based on the least squares and gradient iterations

Proceedings of the IEEE Conference on Decision and Control

Volumn , Issue , 2009, Pages 3632-3636

  Wang, Dongqing ^b   Chu, Yanyun ^b   Ding, Feng ^a  

^a JIANGNAN UNIVERSITY   (China)

^b QINGDAO UNIVERSITY   (China)

Author keywords

Hammerstein models; Iterative identification; Least squares; Parameter estimation; Recursive identification; Stochastic gradient; System modelling; Wiener models

Indexed keywords

COST FUNCTIONS; ITERATIVE METHODS; LEAST SQUARES APPROXIMATIONS; NONLINEAR SYSTEMS; STOCHASTIC MODELS; STOCHASTIC SYSTEMS;

HAMMERSTEIN MODEL; ITERATIVE IDENTIFICATION; LEAST SQUARE; RECURSIVE IDENTIFICATION; STOCHASTIC GRADIENT; SYSTEM MODELLING; WIENER MODELS;

PARAMETER ESTIMATION;

EID: 77950852695     PISSN: 07431546     EISSN: 25762370     Source Type: Conference Proceeding    
DOI: 10.1109/CDC.2009.5399834     Document Type: Conference Paper

References (17)

1. J. Vörös, Parameter identification of Wiener systems with discontinuous nonlinearities, Systems & Control Letters 44 (5) (2002) 363-372.
2. J. Vörös, Modeling and parameter identification of systems with multisegment piecewise-linear characteristics, IEEE Transactions on Automatic Control 47 (1) (2002) 184-188.
3. J. Vörös, Modeling and identification of Wiener systems with twosegment nonlinearities, IEEE Transactions on Control Systems Technology 11 (2) (2003) 253-257.
4. L.A. Aguirre, M.C.S. Coelho, M.V. Corrêa, On the interpretation and practice of dynamical difference between Hammerstein and Wiener models, in: IEE Proceedings of Control Theory and Applications 152 (4) (2005) 349-356.
5. X.L. Hu, H.F. Chen, Strong consistence of recursive identification for Wiener systems, Automatica 41 (11) (2005) 1905-1916.
6. M. Kozek, S. Sinanović, Identification of Wiener models using optimal local linear models, Simulation Modelling Practice and Theory 16 (8) (2008) 1055-1066.
7. J.L. Figueroa, S.I. Biagiola, O.E. Agamennoni, An approach for identification of uncertain Wiener systems, Mathematical and Computer Modelling 48 (1-2) (2008) 305-315.
8. A. Hagenblad, L. Ljung, A. Wills, Maximum likelihood identification of Wiener models, Automatica 44 (11) (2008) 2697-2705.
9. F. Ding, T. Chen, Hierarchical gradient-based identification of multivariable discrete-time systems, Automatica 41 (2) (2005) 315-325.
10. G. Kapetanios, A note on an iterative least-squares estimation method for ARMA and VARMA models, Economics Letters 79 (3) (2003) 305-312.
11. L. Cvetković, Two-sweep iterative methods, Nonlinear Analysis: Theory, Methods & Applications 30 (1) (1997) 25-30.
12. Z.S. Hou, S.T. JIN, M. Zhao. Iterative learning identification method for the macroscopic traffic flow model, Acta Automatica Sinica 34 (1) (2008) 64-71.
13. F. Ding, T. Chen, Identification of Hammerstein nonlinear ARMAX systems, Automatica 41 (9) (2005) 1479-1489.
14. E.W. Bai, An optimal two-stage identification algorithm for Hammerstein-Wiener nonlinear systems, Automatica 34 (3) (1998) 333-338.
15. D.Q. Wang, F. Ding, Extended stochastic gradient identification algorithms for Hammerstein-Wiener ARMXA Systems, Computers and Mathematics with Applications 56 (12) (2008) 3157-3164.
16. Y. Liu, E.W. Bai, Iterative identification of Hammerstein systems, Automatica 43 (2) (2007) 346-354.
17. V. Cerone, D. Regruto, Parameter bounds for discrete-time Hammerstein models with bounded output errors, IEEE Transactions on Automatic Control 48 (10) (2003) 1855-1860.