Adaptive kernel canonical correlation analysis algorithms for nonparametric identification of wiener and hammerstein systems

Eurasip Journal on Advances in Signal Processing

Volumn 2008, Issue , 2008, Pages

  Van Vaerenbergh, Steven ^a   Vía, Javier ^a   Santamaría, Ignacio ^a  

^a UNIVERSITY OF CANTABRIA   (Spain)

Author keywords

[No Author keywords available]

Indexed keywords

ADAPTIVE ALGORITHMS; COMPUTATIONAL EFFICIENCY; COMPUTER SIMULATION; CORRELATION METHODS; PROBLEM SOLVING; TIME VARYING SYSTEMS;

HAMMERSTEIN SYSTEMS; KERNEL CANONICAL CORRELATION ANALYSIS (KCCA); NONPARAMETRIC IDENTIFICATION;

NONLINEAR SYSTEMS;

EID: 43949090190     PISSN: 16876172     EISSN: 16876180     Source Type: Journal    
DOI: 10.1155/2008/875351     Document Type: Article

References (37)

1. Giannakis G., Serpedin E., A bibliography on nonlinear system identification Signal Processing 2001 81 3 533 580
2. Nelles O., Nonlinear System Identification 2000 Berlin, Germany Springer
3. Kechriotis G., Zervas E., Manolakos E. S., Using recurrent neural networks for adaptive communication channel equalization IEEE Transactions on Neural Networks 1994 5 2 267 278
4. Sands N. P., Cioffi J. M., Nonlinear channel models for digital magnetic recording IEEE Transactions on Magnetics 1993 29 6 3996 3998
5. Balestrino A., Landi A.
6. Erdogmus D.
7. Adali T.
8. Pawlak M.
9. Wang J., Sano A., Chen T., Huang B., Blind Hammerstein identification for MR damper modeling Proceedings of the American Control Conference (ACC '07) July 2007 New York, NY, USA 2277 2282
10. Aschbacher E.
11. Westwick D. T., Kearney R. E., Identification of a Hammerstein model of the stretch reflex EMG using separable least squares 3 Proceedings of the 22nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (IEMBS '00) July 2000 Chicago, Ill, USA 1901 1904
12. Cousseau J. E.
13. Taleb A., Sole J., Jutten C., Quasi-nonparametric blind inversion of Wiener systems IEEE Transactions on Signal Processing 2001 49 5 917 924
14. Gomez J. C., Baeyens E., Subspace-based blind identification of IIR Wiener systems Proceedings of 15th European Signal Processing Conference (EUSIPCO '07) September 2007 Poznań, Poland
15. Schlkopf B., Smola A. J., Learning with Kernels 2002 Cambridge, Mass, USA MIT Press
16. Haykin S., Neural Networks: A Comprehensive Foundation 1999 2nd Englewood Cliffs, NJ, USA Prentice-Hall
17. Kimeldorf G. S., Wahba G., Some results on Tchebycheffian spline functions Journal of Mathematical Analysis and Applications 1971 33 1 82 95
18. Bach F. R., Jordan M. I., Kernel independent component analysis Journal of Machine Learning Research 2003 3 1 48
19. Hardoon D. R., Szedmak S., Shawe-Taylor J., Canonical correlation analysis: an overview with application to learning methods 2003 CSD-TR-03-02 Egham, Surrey, UK Royal Holloway, University of London
20. Lai P. L., Fyfe C., Kernel and nonlinear canonical correlation analysis International Journal of Neural Systems 2000 10 5 365 377
21. Hotelling H., Relations between two sets of variates Biometrika 1936 28 321 377
22. Van Vaerenbergh S., Vía J., Santamaría I., Online kernel canonical correlation analysis for supervised equalization of Wiener systems Proceedings of the International Joint Conference on Neural Networks (IJCNN '06) July 2006 Vancouver, BC, Canada 1198 1204
23. Vía J., Santamaría I., Pérez J., Canonical correlation analysis (CCA) algorithms for multiple data sets: application to blind SIMO equalization Proceedings of the 13th European Signal Processing Conference (EUSIPCO '05) September 2005 Antalya, Turky
24. Tikhonov A., Solution of incorrectly formulated problems and the regularization method Soviet Mathematics Doklady 1963 4 1035 1038
25. Van Vaerenbergh S.
26. Diamantaras K. I., Kung S. Y., Principal Component Neural Networks: Theory and Applications 1996 New York, NY, USA John Wiley Sons
27. Schlkopf B., Smola A., Müller K.-R., Nonlinear component analysis as a kernel eigenvalue problem Neural Computation 1998 10 5 1299 1319
28. Engel Y., Mannor S., Meir R., The kernel recursive least squares algorithm IEEE Transactions on Signal Processing 2004 52 8 2275 2285
29. Haykin S., Adaptive Filter Theory 2001 4th Englewood Cliffs, NJ, USA Prentice-Hall
30. Vía J.
31. Pezeshki A., Scharf L. L., Azimi-Sadjadi M. R., Hua Y., Two-channel constrained least squares problems: solutions using power methods and connections with canonical coordinates IEEE Transactions on Signal Processing 2005 53 1 121 135
32. Vía J.
33. Kivinen J., Smola A. J., Williamson R. C., Online learning with kernels IEEE Transactions on Signal Processing 2004 52 8 2165 2176
34. Van Vaerenbergh S., Vía J., Santamaría I., Nonlinear system identification using a new sliding-window kernel RLS algorithm Journal of Communications 2007 2 3 1 8
35. Hoegaerts L.
36. Andeli E.
37. Santamaría I., Ibáez J., Lázaro M., Pantalen C., Vielva L., Modeling nonlinear power amplifiers in OFDM systems form subsampled data: a comparative study using real measurements EURASIP Journal on Applied Signal Processing 2003 2003 12 1219 1228