An online method for detecting nonlinearity within a signal

被引：0
|
作者：
Jelfs, Beth [1 ]
Vayanos, Phebe
Chen, Mo
Goh, Su Lee
Boukis, Christos
Gautama, Temujin
Rutkowski, Tomasz
Kuh, Tony
Mandic, Danilo
机构：
[1] Univ London Imperial Coll Sci Technol & Med, London, England
[2] Phillips Leuven, Louvain, Belgium
[3] Univ Hawaii, Honolulu, HI 96822 USA
来源：
KNOWLEDGE-BASED INTELLIGENT INFORMATION AND ENGINEERING SYSTEMS, PT 3, PROCEEDINGS | 2006年 / 4253卷
关键词：
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
A novel method for online analysis of the changes in signal modality is proposed. This is achieved by tracking the dynamics of the mixing parameter within a hybrid filter rather than the actual filter performance. An implementation of the proposed hybrid filter using a combination of the Least Mean Square (LMS) and the Generalised Normalised Gradient Descent (GNGD) algorithms is analysed and the potential of such a scheme for tracking signal nonlinearity is highlighted. Simulations on linear and nonlinear signals in a prediction configuration support the analysis. Biological applications of the approach have been illustrated on EEG data of epileptic patients.
引用
收藏
页码：1216 / 1223
页数：8
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