NDLI: A nonlinear prediction approach for system identification using chaos symbolic dynamic

Content Provider	IEEE Xplore Digital Library
Author	Nan Xie Leung, H.
Copyright Year	2003
Description	Author affiliation: Dept. of Electr. & Comput. Eng., Calgary Univ., Alta., Canada (Nan Xie; Leung, H.)
Abstract	In this paper we propose using a nonlinear prediction approach to identify an autoregressive (AR) system with chaos symbolic driven signal. This problem widely exists in many practical situations such as channel equalization for chaos digital communications. Although statistic-based techniques can be used to identify systems driven by chaos symbolic signals, they may not fully exploit the information contained in a deterministic chaos symbolic signal and may not result in an optimal solution. In fact, the nonlinear dynamic of a chaos symbolic signal could be properly approximated by using a radial basis function (RBF) net. Based on the short-term predictability of a chaos symbolic signal, an efficient inverse filtering identification approach is proposed. More precisely, a nonlinear prediction error criterion is used as an objective function in the inverse filtering blind identification method. Compared to the statistically optimal least square (LS) method, the proposed nonlinear predictive method is shown to greatly improve the AR system identification performance. We further apply it to combat channel distortions in a digital chaos communication system. It is found that the proposed method has satisfactory equalization performance even when channel effect is strong.
Sponsorship	Syst., Man & Cybernetics Soc. IEEE
Starting Page	1365
Ending Page	1370
File Size	415946
Page Count	6
File Format	PDF
ISBN	0780379527
ISSN	1062922X
DOI	10.1109/ICSMC.2003.1244602
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2003-10-08
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	System identification Chaos Nonlinear dynamical systems Chaotic communication Signal processing Filtering Digital communication Least squares approximation Least squares methods Nonlinear distortion
Content Type	Text
Resource Type	Article

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