Blind maximum-likelihood identification of wiener systems

IEEE Transactions on Signal Processing

Volumn 57, Issue 8, 2009, Pages 3017-3029

  Vanbeylen, Laurent ^a   Pintelon, Rik ^a   Schoukens, Johan ^a  

^a VRIJE UNIVERSITEIT BRUSSEL   (Belgium)

Author keywords

Blind identification; Identification; Maximum likelihood estimation; Nonlinearities; Parameter estimation; Wiener

Indexed keywords

ASYMPTOTIC PROPERTIES; BLIND IDENTIFICATION; BLIND IDENTIFICATIONS; DISCRETE-TIME; GAUSSIANS; HIGH QUALITY; IDENTIFICATION; INITIAL ESTIMATE; LOWER BOUNDS; MAXIMUM-LIKELIHOOD; NONLINEARITIES; SIMULATION EXAMPLE; STATIC NON-LINEARITY; TWO-STEP PROCEDURE; WHITE GAUSSIAN NOISE; WIENER; WIENER SYSTEMS;

BLIND EQUALIZATION; COMMUNICATION CHANNELS (INFORMATION THEORY); CONTROL THEORY; PARAMETER ESTIMATION;

MAXIMUM LIKELIHOOD ESTIMATION;

EID: 68249142936     PISSN: 1053587X     EISSN: None     Source Type: Journal    
DOI: 10.1109/TSP.2009.2017001     Document Type: Article

References (31)

1. S. J. Norquay, A. Palazoglu, and J. A. Romagnoli, "Application of Wiener model predictive control (WMPC) to a pH neutralizing experiment," IEEE Trans. Control Syst. Technol., vol. 7, no. 4, pp. 437-445, 1999.
2. C. J. Clark, G. Chrisikos, M. S. Muha, A. A. Moulthrop, and C. P. Silva, "Time-domain envelope measurement technique with application to wideband power amplifier modeling," IEEE Trans. Microw. Theory Tech. vol. 46, no. 12, pp. 2531-2539, 1998.
3. A. Taleb, J. Solé, and C. Jutten, "Quasi-nonparametric blind inversion of Wiener systems," IEEE Trans. Signal Process., vol. 49, no. 5, pp. 917-924, May 2001.
4. I. W. Hunter and M. J. Korenberg, "The identification of nonlinear biological systems: Wiener and Hammerstein cascade models," Biol. Cybern., vol. 55, pp. 135-144, 1986.
5. S. A. Billings and S. Y. Fakhouri, "Identification of systems containing linear dynamic and static nonlinear elements," Automatica vol. 18, no. 1, pp. 15-26, 1982.
6. J. Schoukens, J. G. Nemeth, P. Crama, Y. Rolain, and R. Pintelon, "Fast approximate identification of nonlinear systems," Automatica, vol. 39, no. 7, pp. 1267-1274, 2003.
7. B. Peeters and G. De Roeck, "Stochastic system identification for operational modal analysis: A review," J. Dyn. Syst., Meas., Control vol. 123, no. 4, pp. 659-667, 2001.
8. K. Abed-Meraim, W. Qiu, and Y. Hua, "Blind system identification," Proc. IEEE, vol. 85, no. 8, pp. 1310-1322, 1997.
9. L. Tong and S. Perreau, "Multichannel blind identification: From subspace to maximum likelihood methods," Proc. IEEE, vol. 86, no. 10, pp. 1951-1968, 1998.
10. N. Kalouptsidis and P. Koukoulas, "Blind identification of Volterra-Hammerstein systems," IEEE Trans. Signal Process., vol. 53, no. 8, pp. 2777-2787, Aug. 2005.
11. E. W. Bai, "A blind approach to the Hammerstein-Wiener model identification," Automatica, vol. 38, no. 6, pp. 967-979, 2002.
12. S. Prakriya and D. Hatzinakos, "Blind identification of LTI-ZMNL-LTI nonlinear channel models," IEEE Trans. Signal Process., vol. 43, no. 12, pp. 3007-3013, Dec. 1995.
13. P. Banelli, "Theoretical analysis and performance of OFDM signals in nonlinear fading channels," IEEE Trans. Wireless Commun., vol. 2, no. 2, pp. 284-293, 2003.
14. L. Ljung, System Identification: Theory for the User, 2nd ed. Upper Saddle River, NJ: Prentice-Hall, 1999.
15. R. Fletcher, Practical Methods of Optimization, 2nd ed. New York: Wiley, 1991.
16. R. Pintelon, J. Schoukens, and G. Vandersteen, "Frequency domain system identification using arbitrary signals," IEEE Trans. Autom. Control vol. 42, no. 12, pp. 1717-1720, 1997.
17. W. Kaplan, Advanced Calculus, 4th ed. Reading, MA: Addison-Wesley, 1993, sec. 2.2.
18. R. Pintelon and J. Schoukens, "Box-Jenkins identification revisited - Part I: Theory," Automatica, vol. 42, no. 1, pp. 63-75, 2006.
19. R. Pintelon and J. Schoukens, System Identification, A Frequency Domain Approach. New York: IEEE Press, 2001.
20. A. de la Torre, A. M. Peinado, J. C. Segura, J. L. Perez-Cordoba, M. C. Benitez, and A. J. Rubio, "Histogram equalization of speech representation for robust speech recognition," IEEE Trans. Speech Audio Process., vol. 13, no. 3, pp. 355-366, 2005.
21. I. N. Lin'kov, Lectures in Mathematical Statistics. Providence, RI: American Math. Soc., 2005.
22. R. Pintelon, J. Schoukens, G. Vandersteen, and Y. Rolain, "Identification of invariants of (over)parameterized models: Finite sample results," IEEE Trans. Autom. Control, vol. 44, no. 5, pp. 1073-1077, 1999.
23. F. Tjárnström, "Computing uncertainty regions with simultaneous confidence degree using bootstrap," in Proc. 12th IFAC Symp. System Identification, Santa Barbara, CA, 2000, pp. 522-527.
24. H. Wold, A Study in the Analysis of Stationary Time Series. Stockholm, Sweden: Almqvist & Wiksell, 1954.
25. F. J. Harris, "On the use of windows for harmonic analysis with the discrete Fourier transform," Proc. IEEE, vol. 66, no. 1, pp. 51-83, 1978.
26. D. R. Brillinger, Time Series: Data Analysis and Theory. New York: McGraw-Hill, 1981.
27. A. M. Mathai and S. B. Provost, Quadratic Forms in Random Variables: Theory and Applications. New York: Marcel Dekker, 1992.
28. P. Doukhan and J. R. León, "Cumulants for stationary mixing random sequences and applications to empirical spectral density," Prob. Math. Stat., vol. 10, no. 1, pp. 11-26, 1989.
29. J. Fan and Q. Yao, Nonlinear Time Series: Nonparametric and Parametric Methods. New York: Springer, 2003.
30. R. Bradley, "Basic properties of strong mixing conditions," Probab. Surv., vol. 2, pp. 107-144, 2005.
31. M. Kendall and A. Stuart, The Advanced Theory of Statistics, 4th ed. London, U.K.: Griffin, 1979, vol. 2.