Neural network training using ant algorithm in ATM traffic control

To maintain the QoS using the traditional mathematical approaches to build an efficient network traffic controller in ATM traffic control is a difficult task. The advantage of using NNs is that the QoS can be accurately estimated without detailed user action models or knowledge about the switching system architecture. The disadvantage is that it will take longer time to train with ATM network changes. In this paper, we use an algorithm in neural network weights training for ATM Call Admission Control (CAC) and Usage Parameter Control (UPC). The simulation results show that this approach is efficient and feasible