A Generalised Mixed Norm Stochastic Gradient Algorithm

Author

Boukis, C. ; Mandic, D.P. ; Constantinides, A.G.

Author_Institution

Athens Inf. Technol., Athens

fYear

2007

fDate

1-4 July 2007

Firstpage

27

Lastpage

30

Abstract

A novel stochastic gradient algorithm for finite impulse response (FIR) adaptive filters, termed the least sum of exponentials (LSE), is introduced. In order to provide a generalisation of the class of weighted mixed norm algorithms and at the same time avoid problems associated with a large number of free paramaters of such algorithms, LSE is derived by minimising a sum of error exponentials. A rigourous mathematical analysis is provided, resulting in closed form expressions for the optimal weights and the upper bound of the learning rate. The analysis is supported by simulations in a system identification setting.

Keywords

FIR filters; adaptive filters; gradient methods; stochastic processes; adaptive filters; closed form expressions; error exponentials; finite impulse response filters; generalised mixed norm algorithm; least sum of exponentials; stochastic gradient algorithm; system identification setting; Adaptive filters; Cost function; Finite impulse response filter; Grid computing; Information technology; Mathematical analysis; Noise robustness; Stochastic processes; System identification; Upper bound; Adaptive Filtering; FIR filters; Gradient Descent Algorithms;

fLanguage

English

Publisher

ieee

Conference_Titel

Digital Signal Processing, 2007 15th International Conference on

Conference_Location

Cardiff

Print_ISBN

1-4244-0882-2

Electronic_ISBN

1-4244-0882-2

Type

conf

DOI

10.1109/ICDSP.2007.4288510

Filename

4288510