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

fYear

2008

fDate

March 31 2008-April 4 2008

Firstpage

2061

Lastpage

2064

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;

fLanguage

English

Publisher

ieee

Conference_Titel

Acoustics, Speech and Signal Processing, 2008. ICASSP 2008. IEEE International Conference on

Conference_Location

Las Vegas, NV

ISSN

1520-6149

Print_ISBN

978-1-4244-1483-3

Electronic_ISBN

1520-6149

Type

conf

DOI

10.1109/ICASSP.2008.4518046

Filename

4518046