Title :
A fuzzy pattern recognition approach for dynamic systems diagnosis. Application to a model of the French telephone network
Author :
Boutleux, Emmanuel ; Dubuisson, Bernard
Author_Institution :
Heudiasyc, Univ. de Technol. de Compiegne, France
Abstract :
Diagnostic methods for the functional state of a static system are well-known. However, the diagnosis of a dynamic process is more difficult to manage because the system state evolves in time. In this paper, a complex system is assumed to evolve from one functional state to another by passing through intermediate states distributed according to a specific path in a multidimensional space. This space is defined from the relevant parameters observed in the system. In order not to create a copious number of intermediate functional states, a two-step decision process based upon fuzzy pattern recognition is proposed. It consists of building membership functions along the path according to which the system state evolves from one known functional state to another. These multidimensional membership functions are used to diagnose the system state. As an example, an application of this method to a model of the French long distance telephone network is illustrated
Keywords :
decision theory; fuzzy set theory; large-scale systems; pattern recognition; probability; sensor fusion; state estimation; state-space methods; telephone networks; French telephone network; data fusion; dynamic systems diagnosis; evolutionary complex system; functional state diagnosis; fuzzy pattern recognition; membership functions; probability; reliability; state space; system state diagnosis; two-step decision process; Fuzzy systems; Knowledge management; Pattern recognition; Real time systems; Recruitment; Sensor systems; Signal processing; Telecommunication traffic; Telephony; Traffic control;
Conference_Titel :
Systems, Man, and Cybernetics, 1996., IEEE International Conference on
Conference_Location :
Beijing
Print_ISBN :
0-7803-3280-6
DOI :
10.1109/ICSMC.1996.561319