DocumentCode :
417471
Title :
TOM-based blind identification of cubic nonlinear systems
Author :
Tan, Hong-Zhou ; Aboulnasr, Tyseer
Author_Institution :
Sch. of Inf. Technol. & Eng., Ottawa Univ., Ont., Canada
Volume :
2
fYear :
2004
fDate :
17-21 May 2004
Abstract :
In this paper, we extend our previous studies on blind cubic nonlinear system identification from the second-order moment (SOM) domain into the third-order moment (TOM) domain. It is shown that under the given sufficient conditions, more subsets of truncated sparse Volterra systems can be blindly identified using TOM instead of SOM. This is consistent with the fact that more statistical knowledge can be obtained in the third-order statistics domain for blind system identification. Simulation results confirm the validity and usefulness of our proposed algorithm.
Keywords :
Volterra equations; identification; method of moments; signal processing; statistics; SOM statistical knowledge; TOM-based blind identification; blind cubic nonlinear system identification; second-order moment domain; signal processing techniques; sparse Volterra system truncated subsets; third-order moment; Biomedical signal processing; Information technology; Kernel; Nonlinear systems; Signal processing; Signal processing algorithms; Sparse matrices; Statistics; Sufficient conditions; System identification;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Acoustics, Speech, and Signal Processing, 2004. Proceedings. (ICASSP '04). IEEE International Conference on
ISSN :
1520-6149
Print_ISBN :
0-7803-8484-9
Type :
conf
DOI :
10.1109/ICASSP.2004.1326397
Filename :
1326397
Link To Document :
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