A new class of nonlinear filters-neural filters

Author

Yin, Lin ; Astola, Jaakko ; Neuvo, Yrjö

Author_Institution

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

Volume

41

Issue

3

fYear

1993

fDate

3/1/1993 12:00:00 AM

Firstpage

1201

Lastpage

1222

Abstract

A class of nonlinear filters based on threshold decomposition and neural networks is defined. It is shown that these neural filters include all filters defined either by continuous functions, such as linear finite impulse response (FIR) filters, or by Boolean functions, such as generalized stack filters. Adaptive least-mean-absolute-error and adaptive least-mean-square-error algorithms are derived for determining optimal neural filters. As special cases, adaptive generalized stack and adaptive generalized weighted order statistic filtering algorithms under both error criteria are derived. Experimental results in 1D and 2D signal processing are presented to compare the performances of the adaptive neural filters and other widely used filters

Keywords

adaptive filters; digital filters; image reconstruction; least squares approximations; neural nets; 1D signal processing; 2D signal processing; adaptive generalized weighted order statistic filtering algorithms; adaptive least-mean-absolute-error algorithms; adaptive least-mean-square-error algorithms; digital filters; generalized stack filters; image restoration; linear FIR filters; neural filters; nonlinear filters; threshold decomposition; Adaptive filters; Additive noise; Boolean functions; Computational complexity; Filtering algorithms; Finite impulse response filter; Mean square error methods; Neural networks; Nonlinear filters; Signal processing algorithms;

fLanguage

English

Journal_Title

Signal Processing, IEEE Transactions on

Publisher

ieee

ISSN

1053-587X

Type

jour

DOI

10.1109/78.205724

Filename

205724