Hybrid neural network pattern recognition system for satellite measurements

Author

Waldemark, Joakim ; Dovner, Per-Ola ; Karlsson, Jan

Author_Institution

Dept. of Appl. Phys. & Electron., Umea Univ., Sweden

Volume

1

fYear

1995

fDate

Nov/Dec 1995

Firstpage

195

Abstract

This paper presents a lower-hybrid cavity detection system (CDS). The CDS is used to analyse measurements of electron plasma density made by the FREJA satellite wave experiment. The system can reduce the amount of data to be analysed by as much as 96% and still retain more than 85% of the desired information. The CDS is a combination of a hybrid neural network (HNN), and expert rules. The HNN is a self organizing map, combined with a feedforward backpropagation neural net. The CDS can be controlled by the user to operate with various degrees of sensitivity. Maximum detection capability is as high as 95% with data reduction of about 85%

Keywords

atmospheric measuring apparatus; aurora; backpropagation; computerised instrumentation; expert systems; feedforward neural nets; geophysical signal processing; geophysics computing; magnetosphere; pattern recognition; plasma density; self-organising feature maps; FREJA satellite wave experiment; backpropagation; cavity detection system; data reduction; electron plasma density; expert rules; feedforward neural net; hybrid neural network; pattern recognition; self organizing map; space plasma physics; Data analysis; Density measurement; Electrons; Information analysis; Neural networks; Organizing; Pattern recognition; Plasma density; Plasma measurements; Satellites;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks, 1995. Proceedings., IEEE International Conference on

Conference_Location

Perth, WA

Print_ISBN

0-7803-2768-3

Type

conf

DOI

10.1109/ICNN.1995.488092

Filename

488092