An adaptive algorithm for Hammerstein filter system identification

European Signal Processing Conference

Volumn , Issue , 2008, Pages

  Umoh, Ifiok J ^a   Ogunfunmi, Tokunbo ^a  

^a Santa Clara University   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

HAMMERSTEIN FILTER; MEAN SQUARE; OPTIMUM PERFORMANCE; STEP SIZE; VARIABLE STEP SIZE;

SIGNAL PROCESSING;

ADAPTIVE ALGORITHMS;

EID: 76749170074     PISSN: 22195491     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (18)

1. R. K. Pearson and M. Pottmann, "Gray-box identification of block-oriented nonlinear models," J. Process. Contr., vol. 10, no. 4, pp. 301-315, 2000.
2. A. Balestrino, A. Landi, M. Ould-Zmirli and L. Sani, "Automatic nonlinear auto-tuning method for Hammerstein modeling of electical drives," IEEE Trans. Ind. Electron., vol. 48, pp. 645-655, 2001.
3. H. W. Kang, Y. S. Cho and D. H. Youn, "On compensating nonlinear distortions of an OFDM system using an efficient adaptive predistorter," IEEE Trans. Commun., vol. 47, no. 4, pp. 522-526, 1999.
4. K. J. Hunt, M. Munih, N. de. N. Donaldson and F. M. D. Barr, "Investigation of the Hammerstein hypothesis in the modeling of electrically stimulated muscle," IEEE Trans. Biomed. Eng., vol. 45, no. 8, pp. 998-1009, 1998.
5. T. Ogunfunmi, Adaptive nonlinear system identification: Volterra and Wiener model approaches. Springer-Verlag, September 2007.
6. J. Voros, "Iterative algorithm for parameter identification of Hammerstein systems with two-segment nonlinearities," IEEE Trans. Autom. Control, vol. 44, no. 11, pp. 2145-2149, November 1999.
7. F. Ding, Y. Shi and T. Chen, "Gradient based identification methods for Hammerstein nonlinear ARMAX models," Nonlinear Dynamics, vol. 45, pp. 31-43, 2005.
8. W. Greblicki, "Continuous-time Hammerstein system identification," IEEE Trans. Autom. Control, vol. 45, pp. 1232-1236, June 2000.
9. J. Jeraj and V. J. Mathews, "A stable adaptive Hammerstein filter employing partial orthogonalization of the input signals," IEEE Trans. Signal Process., vol. 54, pp. 1412-1420, April 2006.
10. H. W. Kang, Y. S. Cho and D. H. Youn, "Adaptive precompensation of Wiener systems," IEEE Trans. Signal Process., vol. 46, pp. 2825-2829, October 1998.
11. E. W. Bai, "Identification of linear systems with hard input nonlinearities of known structure," Automatica, vol. 38, pp. 853-860, 2002.
12. E. Bauer and B. Ninness, "Asymptotic properties of Hammerstein model estimates," in Proc. IEEE Conf. Decision Control, Sydney, Australia, 2000, pp. 2855-2860.
13. E. W. Bai, "A blind approach to the Hammerstein- Wiener model identification," Automatica, vol. 38, pp. 967-976, 2002.
14. M.Boutayeb and M. Darouach, "Recursive identification method for the Hammerstein model: Extension to nonlinear MISO systems," Control Theory Adv. Technol., vol. 10, no. 1, pp. 57-72, 1994.
15. H. Shin, A. H. Sayed and W. Song, "Variable step-size NLMS and affine projection algorithms," IEEE Signal Process. Letters, vol. 11, no. 2, pp. 132 - 135, Febrary 2004.
16. S. Haykin, Adaptive Filter Theory, 4th ed. New Jersey, USA: Prentice Hall, 2002.
17. N. J. Bershad, "On the optimum data nonlinearity of LMS adaptation," IEEE Trans. Acoust., Speech, Signal Process., vol. 34, pp. 69 - 76, Feburary 1986.
18. A. Carini, V. J. Mathews, and G. L. Sicuranza", "Sufficient stability bounds for slowly varying direct-form recursive linear filters and their application in adaptive iir filters," IEEE Trans. Signal Process., vol. 47, no. 9, pp. 2561 - 2567, September 1999.