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

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