Title :
A diagnostic system for the French long distance network using neural trees and a rule-based system
Author :
Didelet, Elisabeth ; Dubuisson, Bernard
Author_Institution :
CNRS, Univ. de Technol. de Compiegne, France
Abstract :
Building a diagnostic system for the French network is a complex pattern recognition problem. A two-level system is proposed to simplify the problem. The first level realizes local diagnosis on each exchange and the second level uses local diagnosis to make a general diagnosis concerning the entire network. Neural trees with ambiguity rejection that represent original nonparametric classifiers are used to build up the first level. A rule-based system is used to implement the second level
Keywords :
diagnostic expert systems; neural nets; nonparametric statistics; pattern recognition; telecommunications computing; telephone networks; French long distance network; ambiguity rejection; diagnostic system; local diagnosis; neural trees; nonparametric classifiers; pattern recognition; rule-based system; two-level system; Cities and towns; Communication system traffic control; Knowledge based systems; Routing; Switches; Telecommunication network management; Telecommunication traffic; Telephony; Time measurement; Traffic control;
Conference_Titel :
Systems, Man and Cybernetics, 1992., IEEE International Conference on
Conference_Location :
Chicago, IL
Print_ISBN :
0-7803-0720-8
DOI :
10.1109/ICSMC.1992.271543
