A study on knowledge discovery method in the design of the GI/G/s network systems

The ISDN has been developed to provide a variety of communication services. As a next step, the broadband ISDN with the asynchronous transfer mode is expected to contribute to the social infrastructure in the 21st century. For the optimum traffic and network designs of the ISDN and the BISDN, it is necessary to evaluate the GI/G/s general traffic (queuing) systems. The notation GI/G/s means s homogeneous parallel switching servers with the general independently distributed interarrival times and the general service time distribution. The exact analytical solution for the GI/G/s model has not yet been obtained. Until now, in order to evaluate the performance indexes of the GI/G/s traffic systems, we have presented the artificial neural method. The method uses a simple three layer feedfoward neural network trained with the backpropagation training algorithm. As a result of the training, the connective weight of the neural network accumulate a lot of information and knowledge relative to the behavior of the GI/G/s system. In this paper, the connective weight is analyzed and interpreted to find important traffic rules of the GI/G/s system. The discovery method is expected to contribute to the designs of 21st networks