Traffic enforcement mechanism in atm using neural network

In our paper, a novel policing mechanism using neural networks (NNs) is proposed. The mechanism is based upon an accurate estimation of the probability density function (pdf) of the traffic via its count process and implemention using NNs. The pdf based policing requires complex calculations in real time, at very high speeds, which is not feasible via conventional mathematical approaches. The proposed traffic enforcement model is elaborate and more accurate method, which exploits the effectiveness of neural network in learning and predicting non-linear complex functions at high speed. It is a suitable tool to be employed for variable bit rate policing of real time traffic in ATM networks.