Fully adaptive neural nonlinear FIR filters

Author

Siaw, Woon Chong ; Lee Goh, Su ; Hanna, Andrew I. ; Boukis, Christos ; Mandic, Dado P.

Author_Institution

Dept. of Electr. & Electron. Eng., Imperial Coll. of Sci., Technol. & Med., London, UK

fYear

2002

fDate

2002

Firstpage

279

Lastpage

288

Abstract

A class of algorithms for training neural adaptive filters employed for nonlinear adaptive filtering is introduced. Sign algorithms incorporated with the fully adaptive normalised nonlinear gradient descent (SFANNGD) algorithm, normalised nonlinear gradient descent (SNNGD) algorithm and nonlinear gradient descent (SNGD) algorithm are proposed. The SFANNGD, SNNGD and the SNGD are derived based upon the principle of the sign algorithm used in the least mean square (LMS) filters. Experiments on nonlinear signals confirm that SFANNGD, SNNGD and the SNGD algorithms perform on par as compared to their basic algorithms but the sign algorithm decreases the overall computational complexity of the adaptive filter algorithms.

Keywords

FIR filters; adaptive filters; adaptive signal processing; computational complexity; filtering theory; gradient methods; least mean squares methods; nonlinear filters; LMS filters; adaptive filter algorithms; adaptive neural nonlinear FIR filters; adaptive normalised nonlinear gradient descent; computational complexity; least mean square filters; neural adaptive filters training; nonlinear gradient descent; nonlinear signals; normalised nonlinear gradient descent; sign algorithms; Adaptive filters; Computational complexity; Convergence; Cost function; Educational institutions; Filtering algorithms; Finite impulse response filter; Information systems; Mathematical model; Taylor series;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks for Signal Processing, 2002. Proceedings of the 2002 12th IEEE Workshop on

Print_ISBN

0-7803-7616-1

Type

conf

DOI

10.1109/NNSP.2002.1030039

Filename

1030039