DocumentCode :
289900
Title :
Introduction of neural networks in the decentralized detection problems
Author :
Duflos, E. ; Boyer, M.-P. ; Coquard, Y. ; Vanheeghe, P.
Author_Institution :
Dept. Signaux et Syst., Inst. Superieur d´´Electron. du Nord, Lille, France
fYear :
1993
fDate :
17-20 Oct 1993
Firstpage :
650
Abstract :
The problem of decentralized detection appears when various sensors are used simultaneously to collect observations from multiple points apart from each other. In this case, some difficulties such as transmission errors, very high transmission rates or memory requirement may occur when a centralized detection is used. To override these limitations detection structures involving two processing levels have been introduced: the decentralized detection. Unfortunately the solutions proposed to the decentralized problem require many calculations. Basing their approach on the similarity between detection problems and neural networks the authors propose a neural approach to solve the decentralized detection problem with fewer calculations
Keywords :
neural nets; sensor fusion; decentralized detection problems; memory requirement; neural networks; transmission errors; two-level processing; very high transmission rates; Backpropagation algorithms; Conference proceedings; Cybernetics; Equations; Explosions; Filters; Intelligent networks; Neural networks; Sensor fusion; White noise;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Systems, Man and Cybernetics, 1993. 'Systems Engineering in the Service of Humans', Conference Proceedings., International Conference on
Conference_Location :
Le Touquet
Print_ISBN :
0-7803-0911-1
Type :
conf
DOI :
10.1109/ICSMC.1993.385090
Filename :
385090
Link To Document :
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