Hierarchical parameter estimation for a class of MIMO Hammerstein systems based on the reframed models

Applied Mathematics Letters

Volumn 57, Issue , 2016, Pages 13-19

  Wang, Dongqing ^a  

^a QINGDAO UNIVERSITY   (China)

Author keywords

Hammerstein system; Hierarchical identification principle; Least squares; MIMO system; Parameter estimation

Indexed keywords

HIERARCHICAL SYSTEMS; LEAST SQUARES APPROXIMATIONS; LINEAR TRANSFORMATIONS; MATHEMATICAL TRANSFORMATIONS; MATRIX ALGEBRA; MIMO SYSTEMS; NONLINEAR ANALYSIS; NONLINEAR SYSTEMS;

EXTENDED LEAST SQUARES; HAMMERSTEIN SYSTEM; HIERARCHICAL IDENTIFICATION PRINCIPLE; IDENTIFICATION MODEL; LEAST SQUARE; LEAST SQUARES METHODS; MATRIX TRANSFORMATION; MULTI INPUT MULTI OUTPUT;

PARAMETER ESTIMATION;

EID: 84957031722     PISSN: 08939659     EISSN: 18735452     Source Type: Journal    
DOI: 10.1016/j.aml.2015.12.018     Document Type: Article

References (28)

1. J.P. Li, C.C. Hua, Y.G. Tang, and X.P. Guan Stochastic gradient with changing forgetting factor-based parameter identification for Wiener systems Appl. Math. Lett. 33 2014 40 45
2. F. Ding, X.H. Wang, Q.J. Chen, and Y.S. Xiao Recursive least squares parameter estimation for a class of output nonlinear systems based on the model decomposition Circuits Systems Signal Process. 35 2016 10.1007/s00034-015-0190-6
3. X.H. Wang, and F. Ding Recursive parameter and state estimation for an input nonlinear state space system using the hierarchical identification principle Signal Process. 117 2015 208 218
4. C. Wang, and T. Tang Recursive least squares estimation algorithm applied to a class of linear-in-parameters output error moving average systems Appl. Math. Lett. 29 2014 36 41
5. F. Giri, and E.W. Bai Block-Oriented Nonlinear System Identification 2010 Springer
6. L.C. Zhou, X.L. Li, and F. Pan Gradient-based iterative identification for MISO Wiener nonlinear systems: Application to a glutamate fermentation process Appl. Math. Lett. 26 8 2013 886 892
7. M. El-Koujok, M. Benammar, N. Meskin, M. Al-Naemi, and R. Langari Multiple sensor fault diagnosis by evolving data-driven approach Inform. Sci. 259 2014 346 358
8. G. Van der Veen, J.W. Van Wingerden, and M. Verhaegen Global identification of wind turbines using a Hammerstein identification method IEEE Trans. Control Syst. Technol. 21 4 2013 1471 1478
9. F. Ding, Y. Shi, and T. Chen Auxiliary model-based least-squares identification methods for Hammerstein output-error systems Systems Control Lett. 56 5 2007 373 380
10. E.W. Bai, and K. Li Convergence of the iterative algorithm for a general Hammerstein system identification Automatica 46 11 2010 1891 1896
11. J. Vörös Identification of nonlinear cascade systems with time-varying backlash J. Electr. Eng. 62 2 2011 87 92
12. J. Vörös Identification of nonlinear dynamic systems with input saturation and output backlash using three-block cascade models J. Franklin Inst. B 351 12 2014 5455 5466
13. D.Q. Wang, F. Ding, and X.M. Liu Least squares algorithm for an input nonlinear system with a dynamic subspace state space model Nonlinear Dynam. 75 1-2 2014 49 61
14. D.Q. Wang, H.B. Liu, and F. Ding Highly efficient identification methods for dual-rate Hammerstein systems IEEE Trans. Control Syst. Technol. 23 5 2015 1952 1960
15. J.H. Li Parameter estimation for Hammerstein CARARMA systems based on the Newton iteration Appl. Math. Lett. 26 1 2013 91 96
16. J.H. Li, F. Ding, and L. Hua Maximum likelihood Newton recursive and the Newton iterative estimation algorithms for Hammerstein CARAR systems Nonlinear Dynam. 75 1-2 2014 235 245
17. F. Ding System Identification - New Theory and Methods 2013 Science Press Beijing
18. F. Ding System Identification - Performances Analysis for Identification Methods 2014 Science Press Beijing
19. Y. Shi, J. Huang, and B. Yu Robust tracking control of networked control systems: application to a networked DC motor IEEE Trans. Ind. Electron. 60 12 2013 5864 5874
20. Q.Y. Shen, and F. Ding Iterative identification methods for input nonlinear multivariable systems using the key-term separation principle J. Franklin Inst. B 352 7 2015 2847 2865
21. X.H. Wang, and F. Ding Convergence of the recursive identification algorithms for multivariate pseudo-linear regressive systems Internat. J. Adapt. Control Signal Process. 30 2016 10.1002/acs.2642
22. F. Ding, X.M. Liu, and Y. Gu An auxiliary model based least squares algorithm for a dual-rate state space system with time-delay using the data filtering J. Franklin Inst. B 353 2016 10.1016/j.jfranklin.2015.10.025
23. D.Q. Wang, and W. Zhang Improved least squares identification algorithm for multivariable Hammerstein systems J. Franklin Inst. B 352 11 2014 5292 5307
24. H. Salhi, and S. Kamoun A recursive parametric estimation algorithm of multivariable nonlinear systems described by Hammerstein mathematical models Appl. Math. Model. 39 16 2015 4951 4962
25. M. Salimifard, M. Jafari, and M. Dehghani Identification of nonlinear MIMO block-oriented systems with moving average noises using gradient based and least squares based iterative algorithms Neurocomputing 94 2012 22 31
26. M. Jafari, M. Salimifard, and M. Dehghani Identification of multivariable nonlinear systems in the presence of colored noises using iterative hierarchical least squares algorithm ISA Trans. 53 4 2014 1243 1252
27. F. Ding, and T. Chen Hierarchical least squares identification methods for multivariable systems IEEE Trans. Automat. Control 50 3 2005 397 402
28. F. Ding, and T. Chen Hierarchical gradient-based identification of multivariable discrete-time systems Automatica 41 2 2005 315 325