Improved least squares identification algorithm for multivariable Hammerstein systems

Journal of the Franklin Institute

Volumn 352, Issue 11, 2015, Pages 5292-5307

  Wang, Dongqing ^a   Zhang, Wei ^a  

^a QINGDAO UNIVERSITY   (China)

Author keywords

[No Author keywords available]

Indexed keywords

REGRESSION ANALYSIS; VECTORS;

BILINEAR FUNCTIONS; CRITERION FUNCTIONS; HAMMERSTEIN SYSTEM; IDENTIFICATION MODEL; INFORMATION VECTOR; LEAST SQUARES ALGORITHM; LEAST SQUARES IDENTIFICATION; PARAMETER ESTIMATE;

PARAMETER ESTIMATION;

EID: 84945893659     PISSN: 00160032     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.jfranklin.2015.09.007     Document Type: Article

References (36)

1. F. Giri, and E.W. Bai Block-Oriented Nonlinear System Identification 2010 Springer-Verlag Berlin Heidelberg
2. F. Ding, X.P. Liu, and G. Liu Identification methods for Hammerstein nonlinear systems Digit. Signal Process. 21 2 2011 215 238
3. F. Wang, K.Y. Xing, and X.P. Xu Study on method for identification of Hammerstein model J. Syst. Simul. 23 6 2011 1090 1092
4. Y. Gu, and F. Ding Parameter estimation for an input nonlinear state space system with time delay J. Frankl. Inst. 351 12 2014 5326 5339
5. G.Q. Li, and C.Y. Wen Convergence of normalized iterative identification of Hammerstein systems Syst. Control Lett. 60 11 2011 929 935
6. G.Q. Li, C.Y. Wen, W.X. Zheng, and G.S. Zhao Iterative identification of block-oriented nonlinear systems based on biconvex optimization Syst. Control Lett. 79 2015 68 75
7. F. Ding Hierarchical multi-innovation stochastic gradient algorithm for Hammerstein nonlinear system modeling Appl. Math. Model. 37 4 2013 1694 1704
8. D.Q. Wang, and F. Ding Hierarchical least squares estimation algorithm for Hammerstein-Wiener systems IEEE Signal Process. Lett. 19 12 2012 825 828
9. 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
10. J. Vörös Identification of nonlinear dynamic systems with input saturation and output backlash using three-block cascade models J. Frankl. Inst. 351 12 2014 5455 5466
11. J. Vörös Identification of nonlinear cascade systems with time-varying backlash J. Electr. Eng. 62 2 2011 87 92
12. K.P. Deng, and F. Ding Newton iterative identification method for an input nonlinear finite impulse response system with moving average noise using the key variables separation technique Nonlinear Dyn. 76 2 2014 1195 1202
13. J.H. Li Parameter estimation for Hammerstein CARARMA systems based on the Newton iteration Appl. Math. Lett. 26 1 2013 91 96
14. J.H. Li, F. Ding, and L. Hua Maximum likelihood Newton recursive and the Newton iterative estimation algorithms for Hammerstein CARAR systems Nonlinear Dyn. 75 1-2 2014 235 245
15. J.L. Sun, and X.G. Liu A novel APSO-aided maximum likelihood identification method for Hammerstein systems Nonlinear Dyn. 73 1-2 2013 449 462
16. V. Verdult, and M. Verhaegen Kernel methods for subspace identification of multivariable LPV and bilinear systems Automatica 41 9 2005 1557 1565
17. F. Felici, J.W. van Wingerden, and M. Verhaegen Subspace identification of MIMO LPV systems using a periodic scheduling sequence Automatica 43 10 2007 1684 1697
18. J.W. van Wingerden, and M. Verhaegen Subspace identification of bilinear and LPV systems for open- and closed-loop data Automatica 45 2 2009 372 381
19. H.Q. Han, L. Xie, F. Ding, and X.G. Liu Hierarchical least-squares based iterative identification for multivariable systems with moving average noises Math. Comput. Model. 51 9-10 2010 1213 1220
20. J.X. Ma, and F. Ding Recursive relations of the cost functions for the least-squares algorithms for multivariable systems Circuits Syst. Signal Process. 32 1 2013 83 101
21. 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 Syst. Signal Process. 31 6 2012 2167 2177
22. D.Q. Wang, Y.P. Gao, Recursive Maximum likelihood identification method for a multivariable controlled autoregressive moving average system, IMA J. Math. Control Inf. (2015) http://dx.doi.org/10.1093/imamci/dnv021.
23. M. Schmitt, and U. Stilla Maximum-likelihood estimation for multi-aspect multi-baseline SAR interferometry of urban areas ISPRS J. Photogramm. Remote Sens. 87 2014 68 77
24. R. Krummenauer, R. Ferrari, R. Suyama, R. Attux, C. Junqueira, P. Larzabal, P. Forster, and A. Lopes Maximum likelihood-based direction-of-arrival estimator for discrete sources Circuits Syst. Signal Process. 32 5 2013 2423 2443
25. 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. PP 99 2012 1 8
26. K.H. Chan, J. Bao, and W.J. Whiten Identification of MIMO Hammerstein systems using cardinal spline functions J. Process Control 16 7 2006 659 670
27. F. Ikhouane, and F. Giri A unified approach for the parametric identification of SISO/MIMO Wiener and Hammerstein systems J. Frankl. Inst. 351 3 2014 1717 1727
28. 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
29. G.Q. Li, C.Y. Wen, W.X. Zheng, and Y. Chen Identification of a class of nonlinear autoregressive models with exogenous inputs based on kernel machines IEEE Trans. Signal Process. 59 5 2011 2146 2159
30. F. Ding, and T. Chen Hierarchical least squares identification methods for multivariable systems IEEE Trans. Autom. Control 50 3 2005 397 402
31. G.C. Goodwin, and K.S. Sin Adaptive Filtering, Prediction and Control 1984 Prentice-Hall Englewood Cliffs, NJ
32. F. Ding, Y.J. Wang, and J. Ding Recursive least squares parameter identification for systems with colored noise using the filtering technique and the auxiliary model Digit. Signal Process. 37 2015 100 108
33. F. Ding Hierarchical parameter estimation algorithms for multivariable systems using measurement information Inf. Sci. 277 2014 396 405
34. 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
35. C. Wang, and T. Tang Several gradient-based iterative estimation algorithms for a class of nonlinear systems using the filtering technique Nonlinear Dyn. 77 3 2014 769 780
36. H.B. Chen, Y.S. Xiao, and F. Ding Hierarchical gradient parameter estimation algorithm for Hammerstein nonlinear systems using the key term separation principle Applied Mathematics and Computation 247 2014 1202 1210