DocumentCode :
3003888
Title :
Blind Identification of Noncausal ARNMA-Hammestein System
Author :
Wu, Yi-Fan ; Xiao, Yun-Shi ; Li, Rong-Yan
Author_Institution :
Coll. of Electron. & Inf. Eng., Tongji Univ., Shanghai, China
fYear :
2010
fDate :
29-31 Oct. 2010
Firstpage :
1
Lastpage :
5
Abstract :
A novel nonlinear model is proposed for nonlinear system blind identification with infinite memory. This model is a generalization and combination of both ARMA model and Volterra-Hammestein (NMA-Hammestein) model. In the process of blind identification, two steps are involved. First, we focused on the left part (AR) of model form,which could be identified by using the generalized BBR formula and MYW(modified Yule-Walker) normal equation. Then, the problem changes into the blind identification of Volterra-Hammestein model, thus several methods could be employed. The simulation results showed its good estimation performance for nonlinear system.
Keywords :
Volterra series; autoregressive moving average processes; blind source separation; identification; time series; ARMA model; BBR formula; Volterra Hammestein model; blind identification; infinite memory; modified Yule Walker normal equation; noncausal ARNMA Hammestein System; nonlinear model; Biological system modeling; Computational modeling; Equations; Kernel; Mathematical model; Nonlinear systems; Signal processing algorithms;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Multimedia Technology (ICMT), 2010 International Conference on
Conference_Location :
Ningbo
Print_ISBN :
978-1-4244-7871-2
Type :
conf
DOI :
10.1109/ICMULT.2010.5631081
Filename :
5631081
Link To Document :
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