Adaptive weighted order statistic filters using back propagation algorithm

Author

Yin, Lin ; Astola, Jaakko ; Neuvo, Yrjö

Author_Institution

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

fYear

1990

fDate

1-3 May 1990

Firstpage

499

Abstract

An adaptive weighted order statistic (WOS) filter is proposed. It can adaptively estimate the parameters of WOS filters according to its inputs and outputs. Since the number of variables of a WOS filter is equal to its window width, this adaptive algorithm is quite efficient. Another distinct advantage is that the adaptive WOS filter can proceed without use of threshold decomposition, which means that any discrete-time continuous value can be used as the input of the WOS filter. Some deterministic properties of WOS filters are discussed. A neural network structure is designed to realize this special stack filter. A learning algorithm is proposed to obtain the parameters of WOS filters. Some simulation results are presented to demonstrate the performance of the learning algorithm

Keywords

adaptive filters; filtering and prediction theory; learning systems; neural nets; adaptive algorithm; back propagation algorithm; deterministic properties; discrete-time continuous value; learning algorithm; neural network structure; simulation; weighted order statistic filters; Adaptive filters; Adaptive systems; Application software; Backpropagation algorithms; Cost function; Filtering theory; Neural networks; Samarium; Sorting; Statistics;

fLanguage

English

Publisher

ieee

Conference_Titel

Circuits and Systems, 1990., IEEE International Symposium on

Conference_Location

New Orleans, LA

Type

conf

DOI

10.1109/ISCAS.1990.112096

Filename

112096