Adaptive stack filtering by LMS and perceptron learning

Author

Ansari, Nirwan ; Huang, Yuchou ; Lin, Jean-Hsang

Author_Institution

Dept. of Electr. & Comput. Eng., New Jersey Inst. of Technol., Newark, NJ, USA

Volume

4

fYear

1992

fDate

23-26 Mar 1992

Firstpage

57

Abstract

Stack filters are a class of sliding-window nonlinear digital filters that possess the weak superposition property (threshold decomposition) and the ordering property known as the stacking property. They have been demonstrated to be robust in suppressing noise. Two methods are introduced to adaptively configure a stack filter. One is by employing the least mean square (LMS) algorithm and the other is based on perceptron learning. Experimental results are presented to demonstrate the effectiveness of the methods for noise suppression

Keywords

adaptive filters; digital filters; interference suppression; learning (artificial intelligence); least squares approximations; neural nets; LMS algorithm; adaptive stack filters; least mean square; noise suppression; ordering property; perceptron learning; sliding-window nonlinear digital filters; threshold decomposition; weak superposition property; Adaptive filters; Additive noise; Binary sequences; Boolean functions; Digital filters; Filtering; Least squares approximation; Noise robustness; Nonlinear filters; Stacking;

fLanguage

English

Publisher

ieee

Conference_Titel

Acoustics, Speech, and Signal Processing, 1992. ICASSP-92., 1992 IEEE International Conference on

Conference_Location

San Francisco, CA

ISSN

1520-6149

Print_ISBN

0-7803-0532-9

Type

conf

DOI

10.1109/ICASSP.1992.226412

Filename

226412