Fuzzy pattern recognition to characterize evolutionary complex systems. Application to the french telephone network

Author

Boutleux, Emmanuel ; Dubuisson, Bernard

Author_Institution

Univ. de Technol. de Compiegne, France

Volume

2

fYear

1996

fDate

8-11 Sep 1996

Firstpage

780

Abstract

Diagnosis methods for the functional state of a static system are well-known. But the diagnosis of a dynamic process is more difficult to handle because the system state evolves in time. This paper builds membership functions along the paths according to which the system state evolves from one known functional state to another. These multi-dimensional membership functions are used to characterize and to follow the complex system state evolution

Keywords

decision theory; fault diagnosis; fuzzy set theory; large-scale systems; pattern classification; telecommunication network management; telephone traffic; diagnosis methods; dynamic process; evolutionary complex systems; french telephone network; fuzzy pattern recognition; multi-dimensional membership functions; Diagnostic expert systems; Fuzzy set theory; Fuzzy systems; Pattern recognition; Principal component analysis; Sensor phenomena and characterization; Sensor systems and applications; Signal processing; Telephony; Tellurium;

fLanguage

English

Publisher

ieee

Conference_Titel

Fuzzy Systems, 1996., Proceedings of the Fifth IEEE International Conference on

Conference_Location

New Orleans, LA

Print_ISBN

0-7803-3645-3

Type

conf

DOI

10.1109/FUZZY.1996.552279

Filename

552279