Adaptive neural filters

Author

Yin, Lin ; Astola, Jaakko ; Neuvo, Yrjö

Author_Institution

Dept. of Electr. Eng., Tampere Univ. of Technol., Finland

fYear

1991

fDate

30 Sep-1 Oct 1991

Firstpage

503

Lastpage

512

Abstract

The authors introduce a new class of nonlinear filters called neural filters based on the threshold decomposition and neural networks. Neural filters can approximate both linear FIR filters and weighted order statistic (WOS) filters which include median, rank order, and weighted median filters. An adaptive algorithm is derived for determining optimal neural filters under the mean squared error (MSE) criterion. Experimental results demonstrate that if the input signal is corrupted by Gaussian noise adaptive neural filters converge to linear filters and if corrupted by impulsive noise, optimal neural filters become WOS filters

Keywords

adaptive filters; neural nets; Gaussian noise; adaptive algorithm; mean squared error; neural filters; neural networks; nonlinear filters; optimal neural filters; threshold decomposition; weighted order statistic filters; Adaptive algorithm; Adaptive filters; Adaptive systems; Finite impulse response filter; Gaussian noise; Neural networks; Neurons; Noise cancellation; Nonlinear filters; Signal processing algorithms;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks for Signal Processing [1991]., Proceedings of the 1991 IEEE Workshop

Conference_Location

Princeton, NJ

Print_ISBN

0-7803-0118-8

Type

conf

DOI

10.1109/NNSP.1991.239491

Filename

239491