Neural network based model reference controller for active queue management of TCP flows

We discuss here, implementation of a neural network (NN) based model referenced control (MRC) algorithm to improve transient and steady state behavior of transmission control protocol (TCP) flows and active queue management (AQM) routers in a network setting. Based on a fluid theoretical model of a network, two neural networks are trained to control the traffic flow of a bottleneck router. Results show dramatic improvement of the transient and the steady state behavior of the queuing window length. The results are compared to the traditional RED algorithm and the P and PI controllers of classical control theory