Title :
Online tracking of the degree of nonlinearity within complex signals
Author :
Mandic, Danilo P. ; Vayanos, Phebe ; Javidi, Soroush ; Jelfs, Beth ; Aihara, Kazuyuki
Author_Institution :
Imperial Coll. London, London
fDate :
March 31 2008-April 4 2008
Abstract :
A novel method for online tracking of the changes in the non- linearity within complex-valued signals is introduced. This is achieved by a collaborative adaptive signal processing approach by means of a hybrid filter. By tracking the dynamics of the adaptive mixing parameter within the employed hybrid filtering architecture, we show that it is possible to quantify the degree of nonlinearity within complex-valued data. Simulations on both benchmark and real world data support the approach.
Keywords :
adaptive signal processing; learning (artificial intelligence); tracking; adaptive mixing parameter; adaptive signal processing; complex signals; hybrid filter; machine learning; nonlinearity degree; online tracking; Adaptive signal processing; Brain modeling; Collaboration; Data analysis; Educational institutions; Electroencephalography; Least squares approximation; Linearity; Machine learning; Signal processing algorithms; Adaptive signal processing; complex LMS; convex optimisation; machine learning; wind modelling;
Conference_Titel :
Acoustics, Speech and Signal Processing, 2008. ICASSP 2008. IEEE International Conference on
Conference_Location :
Las Vegas, NV
Print_ISBN :
978-1-4244-1483-3
Electronic_ISBN :
1520-6149
DOI :
10.1109/ICASSP.2008.4518046
