Simulation Support and Atm Performance Prediction

The estimation of quality of service through the simulation of traac passing an ATM network requires considerable computational resources due to the rare event nature of the phenomena in question. By training a neural network to mimic the output of the simulator, i.e. to capture the functional relationship between diierent conngurations and loads and their corresponding quality of service, estimates can be produced in under a millisecond as opposed to requiring around half a minute using only the simulation. This speed-up in turn enables interactive applications with smooth and instantaneous feedback and the treatment of considerably bigger ATM networks than before. The system can also be run \back-wards", i.e. given a desired quality of service the system can determine the acceptable loads and conngurations.