Identification of Hammerstein-Wiener models

Automatica

Volumn 49, Issue 1, 2013, Pages 70-81

  Wills, Adrian ^a   Schön, Thomas B ^b   Ljung, Lennart ^b   Ninness, Brett ^a  

^a UNIVERSITY OF NEWCASTLE   (Australia)

^b LINKÖPING UNIVERSITY   (Sweden)

Author keywords

Block oriented models; Dynamic systems; Expectation maximization algorithm; Hammerstein; Maximum Likelihood; Monte Carlo method; Nonlinear models; Particle methods; Smoothing; System identification; Wiener

Indexed keywords

BLOCK-ORIENTED MODELS; EXPECTATION-MAXIMIZATION ALGORITHMS; HAMMERSTEIN; NON-LINEAR MODEL; PARTICLE METHODS; SMOOTHING; WIENER;

DYNAMICAL SYSTEMS; MAXIMUM LIKELIHOOD; MODEL STRUCTURES; MONTE CARLO METHODS;

IDENTIFICATION (CONTROL SYSTEMS);

EID: 84871271512     PISSN: 00051098     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.automatica.2012.09.018     Document Type: Article

References (45)

1. K. Abed-Meraim, W. Qiu, and Y. Hua Blind system identification Proceedings of the IEEE 85 8 1997 1310 1322
2. M.S. Arulampalam, S. Maskell, N. Gordon, and T. Clapp A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking IEEE Transactions on Signal Processing 50 2 2002 174 188
3. E. Bai Identification of linear systems with hard input nonlinearities of known structure Automatica 38 5 2002 853 860
4. E. Bai A blind approach to Hammerstein-Wiener model identification Automatica 38 6 2002 967 979
5. Bauer, Dietmar, & Ninness, Brett (1999). Asymptotic properties of least-squares estimates of Hammerstein-Weiner model structures. Technical Report EE9947. Department of Electrical and Computer Engineering. University of Newcastle.
6. D.S. Bernstein Matrix mathematics 2005 Princeton University Press
7. S.A. Billings, and S.Y. Fakhouri Identification of systems containing linear dynamic and static nonlinear elements Automatica 18 1 1982 15 26
8. L. Breiman Hinging hyperplanes for regression, classification, and function approximation IEEE Transactions on Information Theory 39 3 1993 999 1013
9. P.E. Caines Linear stochastic systems 1988 John Wiley and Sons New York
10. A. Dempster, N. Laird, and D. Rubin Maximum likelihood from incomplete data via the EM algorithm Journal of the Royal Statistical Society, Series B 39 1 1977 1 38
11. J.E. Dennis, and R.B. Schnabel Numerical methods for unconstrained optimization and nonlinear equations 1983 Prentice Hall
12. P.M. Djurić, and S.J. Godsill Special issue on Monte Carlo methods for statistical signal processing IEEE Transactions on Signal Processing 50 2 2002 Guest Eds
13. A. Doucet, N. de Freitas, and N. Gordon Sequential Monte Carlo methods in practice 2001 Springer-Verlag
14. A. Doucet, and A. Johansen A tutorial on particle filtering and smoothing: fifteen years later D. Crisan, B. Rozovsky, The Oxford handbook of nonlinear filtering 2011 Oxford University Press
15. R. Douc, A. Garivier, E. Moulines, and J. Olsson Sequential Monte Carlo smoothing for general state space hidden Markov models Annals of Applied Probability 21 6 2011 2109 2145
16. Randal Douc, and Eric Moulines Limit theorems for weighted samples with applications to sequential Monte Carlo methods The Annals of Statistics 36 5 2008 2344 2376
17. S.H. Gibson, and B. Ninness Robust maximum-likelihood estimation of multivariable dynamic systems Automatica 41 10 2005 1667 1682
18. F. Giri, and E. Bai Block-oriented nonlinear system identification Lecture notes in control and information sciences vol. 404 2010 Springer
19. A. Hagenblad, L. Ljung, and A.G. Wills Maximum likelihood identification of Wiener models Automatica 44 11 2008 2697 2705
20. A.J. Heunis Asymptotic properties of prediction error estimators in approximate system identification Stochastics 24 1988 1 43
21. Hsu, K.; Vincent, T.; Poolla, K. (2006). A kernel based approach to structured nonlinear system identification part i: algorithms. part ii: convergence and consistency. In Proceedings of IFAC symposium on system identification. Newcastle. March.
22. X.L. Hu, Thomas Schön, and Lennart Ljung A basic convergence result for particle filtering IEEE Transactions on Signal Processing 56 4 2008 1337 1348
23. A.D. Kalafatis, L. Wang, and W.R. Cluett Identification of Wiener-type nonlinear systems in a noisy environment International Journal of Control 66 1997 923 941
24. Lennart Ljung System identification: theory for the user 2nd ed. 1999 Prentice-Hall, Inc. New Jersey
25. L. Ljung Convergence analysis of parametric identification methods IEEE Transactions on Automatic Control AC-23 5 1978 770 783
26. Lennart Ljung Identification for control - what is there to learn? Y. Yamamoto, S. Hara, Learning, control and hybrid systems Springer lecture notes in control and information sciences vol. 241 1998 Springer Verlag Berlin 207 221
27. Ljung, L. (2011). MATLAB system identification toolbox users guide, version 9. The Mathworks.
28. Lennart Ljung, and Adrian Wills Issues in sampling and estimating continuous-time models with stochastic disturbances Automatica 2010 10.1016/j.automatica.2010.02.011 Available online 15th March 2010
29. G. McLachlan, and T. Krishnan The EM algorithm and extensions 2nd ed. 2008 John Wiley and Sons
30. K.S. Narendra, and P.G. Gallman In iterative method for the identification of nonlinear systems using a Hammerstein model IEEE Transactions on Automatic Control 11 7 1966 546 550
31. B.M. Ninness Estimation of 1 / f noise IEEE Transactions on Information Theory 44 1 1998 32 46
32. B. Pötscher, and I. Prucha Dynamic nonlinear econometrics models 1997 Springer-Verlag Berlin-Heidelberg
33. R. Raich, G.T. Zhou, and M. Viberg Subspace based approaches for Wiener system identification IEEE Transactions on Automatic Control 50 10 2005 1629 1634
34. B. Ristic, S. Arulampalam, and N. Gordon Beyond the Kalman filter: particle filters for tracking applications 2004 Artech house Boston, MA, USA
35. M. Schetzen The Volterra and Wiener theories of nonlinear systems 1980 Wiley
36. T.B. Schön, A.G. Wills, and B. Ninness System identification of nonlinear state-space models Automatica 37 1 2011 39 49
37. J. Schoukens, J.G. Nemeth, P. Crama, Y. Rolain, and R. Pintelon Fast approximate identification of nonlinear systems Automatica 39 7 2003 1267 1274
38. L.R. Vanbeylen, R. Pintelon, and J. Schoukens Blind maximum likelihood identification of Wiener systems IEEE Transactions on Signal Processing 57 8 2009 3017 3029
39. Peter van Overschee, and Bart De Moor Subspace identification for linear systems - theory, implementation, applications 1996 Kluwer Academic Publishers
40. D. Westwick, and M. Verhaegen Identifying MIMO Wiener systems using subspace model identification methods Signal Processing 52 1996 235 258
41. Wiener, N. (1942). Response of a nonlinear system to noise. Technical report. Radiation Lab MIT 1942. restricted, report V-16. no. 129. (p. 112). Declassified Jul 1946. Published as rep. no. PB-1-58087. US Dept. Commerce.
42. T. Wigren Recursive prediction error identification using the nonlinear Wiener model Automatica 29 4 1993 1011 1025
43. A.G. Wills, and L. Ljung Block-oriented nonlinear system identification Lecture notes in control and information sciences vol. 404 2010 Springer (Chapter Wiener system identification using the maximum likelihood method)
44. A.G. Wills, B. Ninness, and S.H. Gibson Maximum likelihood estimation of state space models from frequency domain data IEEE Transactions on Automatic Control 54 1 2009 19 33
45. Wills, A.G.; Schön, T.B.; Ljung, L.; Ninness, B. (2011). Blind identification of Wiener models. In Proceedings of the IFAC world congress.